AN EMPIRICAL STUDY ON THE USE OF SECURE PREDICTIVE ANALYTICS FOR IMPROVING TRADE FORECASTING IN THE UAE.

Chapter 1: Introduction

1.1 Overview

Global economies are fundamentally very dependent on international trade-in,

which is defined as the exchange of goods and services across international boundaries.

This allows consumers and countries to access a wide variety of products and services

outside of their domestic markets (WTO, 2021). For determining a country's economic

situation and potential for growth, it is essential to analyze trade dynamics, including

imports, exports, and GDP (WTO, 2021). Understanding the connection between GDP,

exports, and imports is extremely necessary for predicting future trends and their effects

on economies. Furthermore, international institutions desire the establishment of the

World Trade Organization (WTO) to foster and encourage commerce among nations, with

varying conditions (WTO, 2021).

The United Arab Emirates' (UAE) imports and exports have evolved significantly

over the past decade, reflecting its dynamic economic journey. From its historical

significance as a crucial crossroads for ancient trade routes to its modern prominence as a

global trade hub, the UAE's trade landscape has witnessed remarkable changes. Early on,

its strategic geographical position facilitated a wide range of trade interactions as per Siiner

(2016). With the discovery of oil, the UAE's trade dynamics began to undergo a seismic

transformation with oil exports propelling its emergence on the global scale (Reynolds &

Umekwe 2019).

The historical import and export trends of the Gulf states that maintain commercial

relations with the United Arab Emirates (UAE) show a narrative of interconnected

economies and shared trade dynamics. Over the past few decades, these trends have

reflected not only the economic evolution of each state, but also the symbiotic

relationships that bind them within the intricate structure of regional trade.

It becomes evident that the Gulf Cooperation Council (GCC) states, including

Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE, have experienced significant

trade changes. In the early years, the region's trade primarily centered on traditional

commodities and regional exchanges. The Gulf states gradually diversified their trade

portfolios, with petroleum and its derivatives becoming the primary source of their exports

(Sturm et al., 2008). This shift was particularly pronounced in Saudi Arabia, where oil

exports played a significant role in shaping its trade landscape.

In addition, recognizing the major trade affiliates of the UAE further illuminates

the intricate network of mutual trade relationships. Although statistical data from sources

such as the United Nations Comtrade database and reports by the World Trade

Organization provide a quantitative perspective, qualitative analysis highlights the

strategic partnerships that have flourished. The UAE's designation as a strategic gateway

and trade hub has created strong alliances with trading partners such as China, India, the

United States, and other GCC members as stated by Chaziza (2019). These affiliations

underscore the multifaceted nature of UAE's trade, spanning various sectors such as

energy, technology, and consumer goods.

In the field of trade forecasting, understanding these historical trends and trade

affiliations is a compass, guiding predictive models to discern future patterns. By

integrating historical data with predictive analytics, the empirical study aims to shed light

on the past, but also on the evolving landscape of Gulf states' trade relations and their

mutual relations with the UAE. In recent years, a strategic shift towards economic

diversification led to a rise in non-oil exports and the development of modern

infrastructure (Schwarz, 2008). Today, the UAE's imports and exports encompass a

diverse range of industries, highlighting its strategic partnerships and robust economic

policies. This evolution provides a foundational basis for our empirical study, where

predictive analytics is poised to illuminate the future of UAE's trade forecasting landscape.

To improve trade forecasting globally, predictive analytics has become a useful tool

for researchers. The implementation of security measures, in these analytics guarantees

the safeguarding of trade information, against access and cyber risks. Researchers have

developed predictive models that provide improved accuracy and reliability in predicting

future trade patterns and trends using historical trade data and advanced analytical

techniques (Zhang et al., 2017). Ensuring the integrity and confidentiality of this data

through secure channels enhances the trustworthiness of the predictions. These modern

models use statistical techniques and machine learning algorithms (Tang et al., 2022) to

find hidden patterns and relationships in trade data, allowing the identification of

important variables affecting trade changing aspects. Predictive models have been used in

studies to examine, for instance, how trade agreements, geopolitical variables, and

macroeconomic indicators affect trade volumes and patterns as stated by Hedström et al.

(2020). The security of these predictive analytics systems is crucial, as they handle a

plethora of sensitive economic and political data. These models can offer an important

understanding of the future course of international trade by including elements like GDP

growth rates, exchange rates, and political stability (Joo & Lee, 2021).

The potential of predictive analytics for enhancing trade forecasting has also been

recognized by researchers in the United Arab Emirates (UAE). Accurate prediction is

essential for the country's economic planning and growth given the UAE's strategic

position as a global trade hub. Some studies based on different models have already been

carried out to anticipate trade volumes detect emerging markets and industries and aid in

investment and policy decision-making pertaining to trade in the UAE. These models have

integrated advanced security protocols to ensure that the trade secrets and strategic insights

remain confidential. They consider trade dynamics, industry diversification, and

geopolitical factors to generate more tailored and accurate forecasts, for the nation.

Many studies have investigated the use of predictive analytics in trade forecasting

to improve resource allocation (Singh & El-Kassar, 2019), pricing strategies (Shrivastava

& Riaz, 2022), and inventory management for companies engaged in the UAE's trade

sector (Al Mesfer, 2023). Incorporating security measures like encryption and access

control ensures that this predictive intelligence is not compromised. Predictive models

have proven to be tools for businesses enabling them to mitigate risks, boost profitability,

and make informed trade decisions. Secure predictive analytics models play a critical role

in this process, protecting trade secrets while enabling companies to anticipate market

movements. By examining the trade data, observing market trends, and understanding

customer behavior, these models provide valuable insights into upcoming trade patterns

and trends. This information is particularly beneficial for investors, businesses, and

policymakers, enabling them to optimize their trade-related endeavors. Consequently,

nations like the UAE can gain from increased growth and enhanced competitiveness.

Over the past few decades, UAE has emerged as a rapidly advancing nation with a

robust economy, largely due to its active trade sector encompassing imports and exports

(Ewers & Madeeha, 2022). Ensuring this growth trajectory requires a secure trade

environment where data and predictive insights remain protected from cyber threats and

intellectual property theft. Numerous forecasts of trade patterns and trends contribute to

the development of strategic economic planning and growth within the UAE. By

facilitating informed decision-making on investments, pricing strategies, inventory

management, and resource allocation, trade forecasting is indispensable. However,

conventional trade forecasting methods often struggle to accurately forecast the intricate

and ever-changing nature of global trade.

The realm of predictive analytics, utilizing the potential of machine learning and

statistical modeling, has become a beacon of success. In recent times, predictive analytics

has enabled policymakers and businesses to make informed decisions, utilizing historical

trade data and advanced analytical methods to provide valuable insight into future trade

dynamics. Despite the numerous advantages predictive analytics provide for trade

forecasting, a certain amount of uncertainty is causing its true effectiveness and

applicability.

Therefore, it is essential to bridge this gap in knowledge through an empirical study

centered on the deployment of predictive analytics to enhance trade forecasting within the

UAE. Such an investigation can shed light on the precision, dependability, and pragmatic

use of predictive analytics techniques within the UAE's unique trade area. By evaluating

the capabilities of predictive analytics models, weighing data accessibility and quality, and

comprehending the skill set essential for seamless integration, this research embarks on a

journey to enhance trade forecasting capabilities in the UAE.

The study examines three distinct objectives: Firstly, to determine if predictive

analytics techniques were beneficial to the accuracy of UAE's trade forecasts while

ensuring the security of the data used. Secondly, to determine the most suitable predictive

analytics models for trade forecasting in the UAE's distinctive context that also adhere to

the highest standards of data security. Thirdly, to calculate the Gross Domestic Product

(GDP) of the UAE and compare it with the forecasted trade and with other developing

countries while keeping all the data and predictive insights secure against unauthorized

access.

This study utilizes historical trade data and various predictive analytics techniques

to provide a quantitative approach. The performance of the predictive model is rigorously

evaluated based on parameters such as accuracy, precision, and recall. Additionally,

qualitative insights are obtained through interviews with trade forecasting experts,

shedding light on the real-world implementation nuances and challenges encountered by

predictive analytics in the UAE.

The results of this study are remarkable, resulting in a significant impact on the

UAE's trade sector. The anticipated outcomes include enhanced decision-making acumen,

refined resource allocation, and a higher competitive edge on the global stage. Equally

important is the assurance of secure data handling, which upholds the integrity of the

UAE's trade intelligence. In addition, the study's contribution reverberates in the academic

field, revealing the nuances that influence the effectiveness of predictive analytics

techniques in various trade forecasting scenarios, with a special emphasis on their security

posture.

1.2 Statement of the Problem

The connection between gross domestic product (GDP) and microeconomic

variables, particularly imports and exports, has been a topic of discussion. This connection

was crucial for informed decision-making regarding investment, pricing, and resource

allocation by the government and businesses in the United Arab Emirates (UAE). Ensuring

the security of the economic data involved in such analyses was paramount, as this

information could be a target for cyber threats aiming to undermine economic stability.

Furthermore, predicting the future direction of UAE trade could have enabled stakeholders

to anticipate and prepare for economic changes. The security of the forecasting models

and the data they used was essential to prevent manipulation and ensure the integrity of

predictions.

Although time series forecasting techniques had been commonly used to forecast

economic indicators like GDP, it remained uncertain whether these techniques could

reliably predict the long-term direction of UAE trade. Given the sensitivity of the data

involved, the application of secure data processing methods and access controls was

necessary to protect against unauthorized access and potential data breaches. Thus, this

study aimed to address the following problem:

1. What was the connection between GDP and microeconomic variables,

specifically imports and exports, in the UAE when GDP was calculated using

the expenditure approach? The reliability of this connection depended on secure

data collection and handling methods to ensure the accuracy of the GDP

calculation.

2. Could time-series prediction models effectively forecast the long-term direction

of UAE trade based on historical data? It was critical to secure the historical

data and the predictive models against cyber-attacks to maintain trust in their

long-term forecasts.

By addressing these questions, this research provided valuable insights into the

factors that influenced the UAE's economy and informed decisions regarding resource

allocation and investment. Furthermore, the study's findings contributed to the existing

academic literature on the relationship between microeconomic variables and GDP as well

as the efficacy of time series forecasting techniques for predicting trade patterns. while

highlighting the importance of data security in economic research.

1.3 Research Questions

The problem statement gave rise to the following research questions, which guided

the investigation and analysis within the study:

1. How did the imports and exports of the UAE evolve over time, providing a

comprehensive understanding of the trade dynamics within the country?

2. What were the past import and export trends of developed countries that had

significant commercial links with the UAE, enabling a comparative analysis of

trade patterns in the region?

3. Which were the major trade affiliates of the UAE for mutual trade, highlighting

the key countries or regions with significant trade relationships with the UAE?

In identifying these, data privacy measures were put in place to protect the

interests and commercial confidentiality of the UAE and its trade partners.

4. What were the primary commodities that the UAE exported abroad, shedding

light on the key sectors and industries that contributed to the country's trade

balance?

5. How was the GDP for UAE collected based on the expenditure approach,

elucidating the methodology employed to calculate the economic output of the

country? Robust cybersecurity measures were essential to safeguard the

collection and processing of economic data, ensuring its accuracy and

reliability.

1.4 Research Objectives

1. To use the Long Short-Term Memory (LSTM) model to implement the

regression and predictive analysis to assess the relationship between trade and

UAE imports/exports. Cryptographically secure protocols will be used to

safeguard the trade data during the analysis.

2. To utilize the selected algorithm for the prediction of UAE exports and imports

under changing scenarios. Here, we can integrate cryptographic techniques such

as data anonymization or homomorphic encryption to ensure the privacy of

sensitive trade data while using the LSTM algorithm for predictions.

3. To calculate the GDP of the UAE based on consumption, expenditure,

investment, and net exports data. Utilize secure data environments and ensure

all GDP-related data is encrypted at rest to protect the confidentiality of

economic indicators.

4. To compare the GDP of the UAE and other high-income countries.

5. To evaluate the effectiveness of the regression analysis in predicting the

relationship between GDP and exports/imports and in predicting trends. Apply

secure computation techniques to maintain data privacy during the regression

analysis, ensuring that insights are derived from encrypted datasets without

exposing sensitive economic data.

6. To identify the main factors that influence trade in the UAE.

7. To provide recommendations to improve the accuracy of trade forecasting in

the UAE.

The study aimed to gain a thorough understanding of the historical patterns and

trends in trade within the UAE and the countries where there is the most trade exchange

by examining these research questions. The research results added to the body of

knowledge on international trade and improved understanding of trade dynamics in the

region by offering insightful information that influenced business and policy decisions.

The integration of cryptographic techniques not only provides clarity on the protection of

sensitive data but also highlights the commitment to ethical research practices that respect

data privacy and security.

1.5 Relevant Literature

The literature review concentrates on studies pertinent to examining the connection

between a nation's GDP and its imports and exports. The topic covered in this literature

review is research done to attempt to predict trade directions using the LSTM model.

1.5.1 Economic Growth Indicators

Trade relationships are an important factor for economic growth, and Gross

Domestic Product (GDP) is an essential measure to assess a country's economic

performance. Imports and exports are critical components of trade relationships, and they

affect a country's balance of payments, economic growth, and international

competitiveness. This literature review explores the relationship between trade

relationships, GDP, imports, and exports.

Trade relationships refer to the economic and political relationships between

countries that facilitate the exchange of goods, services, and capital. The extent and nature

of trade relationships can vary between countries, and they can be influenced by various

factors such as tariffs, trade agreements, and political stability. Studies have shown that

countries with stronger trade relationships tend to have higher levels of economic growth.

For example, research by Linnemann (1966) found that trade relationships were positively

correlated with GDP growth. Furthermore, research by Dollar and Kraay (2003)

demonstrated that an increase in trade relationships was associated with an increase in per

capita income. Moreover, research has shown that there is a positive relationship between

GDP and trade relationships. For example, research by Rodriguez and Rodrik (2000)

found that countries with high levels of trade tend to have higher levels of GDP. Similarly,

research by Frankel and Romer (2017) found that trade openness was positively associated

with economic growth.

Imports and exports are a critical component of trade relationships, and they can

have a significant impact on a country's balance of payments, economic growth, and

international competitiveness. Imports refer to goods and services that are brought into a

country, while exports refer to goods and services that are produced domestically and sold

to other countries. Research has shown that there is a positive relationship between exports

and economic growth. For example, research by Baldwin and Gu (2005) found that an

increase in exports was associated with an increase in GDP. Similarly, research by

Edwards (1998) found that countries with higher levels of exports tend to have higher

levels of economic growth. On the other hand, imports can have a negative impact on a

country's balance of payments and domestic industries. Research has shown that there is a

negative relationship between imports and economic growth. For example, research by

Lee et al. (2004) found that an increase in imports was associated with a decrease in GDP.

Frankel and Romer (2017) examined the two countries' bilateral trade. To gauge

trade between the two countries, the authors employed the Trade Intensity Index (export

and import):

Exports Intensity Index = (ABn / AB) / ((TI / (Tw - YZ)) Equation (1)

where, ABn = 1st country exports to 2nd country

AB = Total exports of 1st country

TI = 2nd country total imports

Tw = Total world imports

YZ = Total imports of 1st country

Import Intensity Index = (ABn / AB) / ((TE / (Tw - YZ)) Equation (2)

where, ABn = 1st country exports to 2nd country

AB = 1st country total imports

TE = 2nd country total exports

Tw = Total world exports

YZ = Total exports of 2nd country

Multiple regression approach was employed in the macroeconomic study for

Bosnia and Herzegovina. This study evaluated the six independent variables to determine

how macroeconomic factors (independent variables) impact gross domestic product

(GDP); growth rate, unemployment, foreign direct investment (FDI), inflation, imports,

and exports and (dependent variable). For their analysis, the authors utilized the following

multiple regression formula:

Y=A_o+ A_1 Z_1+A_2 Z_2+A_3 Z_3+A_4 Z_4+A_5 Z_5+A_6 Z_6+ε Equation (3)

Their multiple linear regression model was presented as follows: GDP is a

dependent variable and inflation, unemployment, growth rate, exports, imports, FDI are

considered as independent variables. ε is used to reflect effects on GDP that are not caused

by the independent variables.

The above analysis conducted by the author indicates that that 93.2% of changes in

GDP can be explained by the independent variables. The researcher concluded that exports

and FDI had the least impact on GDP, whereas imports have the highest impact.

1.5.2 Trade Forecasting

The foundation of economic decision-making is trade forecasting, which leads

countries, companies, and investors through the complex web of global trade. Accurate

trade pattern forecasting gives businesses an advantage in navigating international markets

as well as enabling policymakers to create effective economic strategies. The importance

of accurate and forward-looking trade forecasting increases as the world becomes more

interconnected and trade dynamics become more complex.

Predictive analytics is a transformative strategy that makes use of data-driven

insights to predict future trends because of the fusion of conventional economic theories

and cutting-edge technological advancements. Predictive analytics transcends traditional

statistical methods by exposing hidden patterns, relationships, and potential disruptions in

trade flows. It is frequently paired with its powerful counterpart, machine learning. Its

ability to condense massive amounts of past and present trade data into usable forecasts

represents a sea change in the field of trade analysis.

The complex interplay between macroeconomic forces and microeconomic

behavior emerges as a central theme as we explore the world of trade forecasting and

predictive analytics. The trajectory of international trade is influenced by factors such as

GDP growth, inflation rates, geopolitical events, and consumer preferences. Making

informed decisions at the national and organizational levels is made possible by having a

nuanced understanding of the complex relationship between these variables and trade

patterns.

Figure 1: Classification of literature review

We explore the nuances of predictive analytics and its use in the context of the UAE

as we search into the complexities of trade forecasting. Figure 1 shows how the literature

review is classified into different categories to provide a comprehensive overview that

how scientists are using predictive tools for economic forecasting. We aim to uncover the

underlying dynamics that underlie trade patterns, solve the riddle of predictive analytics,

and provide insightful contributions to the advancement of trade forecasting

methodologies through empirical analysis and a meticulous evaluation of historical trade

data. In order to improve the accuracy and the quality of our decisions in the area of

forecasting UAE trade, we are entering into uncharted data-driven insights.

1.5.2 Theoretical Framework and Concepts

The theoretical framework that underpins predictive analytics forms the bedrock

upon which modern trade forecasting capabilities rest. Drawing from an amalgamation of

established theories, models, and concepts, predictive analytics infuses innovation into

trade forecasting methodologies, yielding enhanced accuracy and actionable insights. As

we delve into the realm of predictive analytics' relevance to trade forecasting, this section

navigates through key theoretical underpinnings while citing original research that has

paved the path for its application.

Time series analysis, a cornerstone in economic modeling, establishes a temporal

dimension to trade forecasting. The work of Geurts (1977) is emblematic in introducing

Autoregressive Integrated Moving Average (ARIMA) models, offering a robust

framework for capturing the intrinsic sequential dependencies within trade data. The

integration of these techniques within predictive analytics lends temporal context to trade

patterns, unveiling cyclical, seasonal, and trend-based fluctuations that impact cross-

border exchanges (GEP, 1970).

Machine learning algorithms, influenced by advancements in computational

capabilities, bring a paradigm shift to trade forecasting methodologies. Notably, the

Random Forest algorithm, introduced by Breiman (2001), ushers in an ensemble approach

that amalgamates numerous decision trees to predict trade patterns with improved

accuracy and robustness. Kleemann and Abdulai (2013) exemplifies the integration of

Random Forest in predicting trade volumes and highlighting influential predictors within

a global trade context.

Regression analysis, a venerable statistical tool, finds renewed vigor in predictive

analytics for trade forecasting. Building upon its traditional applications, regression

analysis embraces the multivariate dimension of predictive analytics, enabling the

assessment of how multiple variables collectively influence trade patterns. Noteworthy is

the study by Javed et al. (2016), which employs regression analysis to explore the

determinants of bilateral trade between the UAE and its trading partners.

Furthermore, concepts of pattern recognition and anomaly detection, hailing from

the realm of artificial intelligence, enrich predictive analytics' ability to discern atypical

trade behaviors that might indicate impending shifts. The pioneering work of Bartos et al.

(2019) on detecting anomalies in time series data is emblematic of this concept's relevance,

offering a mechanism to identify irregular trade occurrences that necessitate prompt

attention.

In essence, the theoretical fabric interwoven with predictive analytics amalgamates

temporal dynamics, computational prowess, and multivariate insight. Time series analysis,

machine learning algorithms, regression analysis, and anomaly detection collectively

scaffold predictive analytics, transforming trade forecasting from a static endeavor into a

dynamic, data-driven discipline. Through the synthesis of these theories, predictive

analytics emerges as a potent lens that illuminates the intricacies of trade patterns,

unfurling a tapestry of insights that elevate the precision and efficacy of trade forecasting

methodologies.

1.5.3 Trade Forecasting Techniques and Approaches

The realm of trade forecasting encompasses a variety of techniques and approaches,

each offering a unique opportunity to anticipate the intricate dynamics of global

commerce. This section focuses on the diverse landscape of trade forecasting

methodologies, from conventional methods to advanced predictive analytics, highlighting

their evolution and real-world applications. Figure 2 shows the division of trade

forecasting methods and techniques that are in use by the researchers in trade forecasting.

Table 1 shows the comparisons of different trade forecasting approaches and

techniques from literature.

Figure 2: Trade forecasting methods

Table 1: Comparison of trade forecasting techniques and approaches

Technique/Approach

Key Features

Application

Traditional Methods

Based on historical data

patterns

Initial insights into trade

trends

Statistical Models

Incorporates temporal

dependencies and

interrelationships

Captures dynamic

interactions among trade

variables

Predictive Analytics

Utilizes machine learning,

deep learning, and

computational power

Enhances forecasting

accuracy and captures

long-term dependencies

Application of Predictive

Analytics

Applies predictive models

to forecast trade volumes

and patterns

Improved forecasting

accuracy in various trade

contexts

1.5.3.1 Traditional Methods

Traditional trade forecasting methods have long served as the cornerstone of

economic analysis. Methods like moving averages, exponential smoothing, and linear

regression have provided initial insights into trade trends by extrapolating historical data

patterns. While offering a rudimentary understanding of trade dynamics, these methods

often fall short in capturing the intricacies of modern trade patterns and their underlying

drivers.

1.5.3.2 Statistical Models

Statistical models, such as autoregressive integrated moving average (ARIMA) and

vector autoregression (VAR), introduced a more sophisticated framework by accounting

for temporal dependencies and interrelationships among trade variables. ARIMA,

championed by 1. Geurts, M. (1977), leverages time series analysis to capture the

sequential patterns in trade data, while VAR extends the analysis to encompass the

dynamic interplay among multiple trade variables.

1.5.3.3 Evolution Towards Predictive Analytics

The emergence of predictive analytics marked a transformative shift in trade

forecasting methodologies. Predictive analytics, underpinned by machine learning

algorithms and advanced computational capabilities, redefined the boundaries of trade

analysis. Notably, the Long Short-Term Memory (LSTM) model within the deep learning

paradigm enabled the incorporation of sequential patterns into trade forecasting. LSTM,

introduced by Hochreiter and Schmidhuber (1997), possesses a unique memory-enhanced

architecture that excels at capturing long-term dependencies in time series data, making it

particularly apt for forecasting trade patterns.

1.5.3.3.1 Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN)

that has been widely used in various applications, including time series forecasting. This

literature review explores the use of LSTM for trade forecasting, with a focus on its

advantages, limitations, and empirical evidence. LSTM has several advantages for trade

forecasting. First, it can capture long-term dependencies in time series data, which is

essential for trade forecasting because trade patterns are often affected by economic and

political factors that may take time to unfold. Second, LSTM can handle non-linear

relationships between input and output variables, which is useful for predicting trade

patterns that may be influenced by complex and interrelated factors. Finally, LSTM can

learn from historical data to generate accurate predictions for future trade patterns. Despite

its advantages, LSTM has some limitations for trade forecasting. First, LSTM requires

large amounts of historical data to learn from, and trade data may be limited or incomplete

for some countries or regions. Second, LSTM may struggle to generalize to unseen data,

and its predictions may be less accurate for countries or regions that have different trade

patterns from those in the training data. Finally, LSTM requires careful tuning of

hyperparameters, and its performance may be sensitive to the choice of hyperparameters.

Several studies have explored the use of LSTM for trade forecasting. For example, a study

by Wang et al. (2019) used LSTM to predict China's trade with the United States and found

that LSTM outperformed traditional time series models. Similarly, a study by

Rasoulinezhad et al. (2020) used LSTM to predict the trade balance of Iran and found that

LSTM improved prediction accuracy compared to traditional time series models. Another

study by Chen et al. (2019) used LSTM to predict China's exports to the United States

during the trade war between the two countries and found that LSTM was able to capture

the complex and dynamic relationships between trade and political factors. Finally, a study

by Bouri et al. (2020) used LSTM to predict the direction of trade for five major economies

and found that LSTM outperformed traditional statistical models. Hu et al. (2021) used an

LSTM model to forecast the monthly trade volume between China and the United States.

Their study showed that the LSTM model outperformed traditional forecasting models in

terms of prediction accuracy. Wu et al. (2020) used an LSTM model to forecast China's

trade with the United States and Japan. Their results showed that the LSTM model had

better prediction accuracy than traditional forecasting models. Li et al. (2019) used an

LSTM model to forecast the volume of China's imports and exports.

Their results showed that the LSTM model outperformed traditional models in

terms of prediction accuracy and was able to capture the non-linear relationships in the

data. Zhang et al. (2020) used an LSTM model to predict the import and export volume of

China's agricultural products. Their results showed that the LSTM model had better

prediction accuracy than traditional models. Ma et al. (2019) used an LSTM model to

forecast the import and export volume of China's liquefied natural gas. Their results

showed that the LSTM model outperformed traditional models in terms of prediction

accuracy. These studies suggest that LSTM is a promising tool for trade forecasting due

to its ability to capture long-term dependencies and handle non-linear relationships in the

data. LSTM has been shown to outperform traditional forecasting models in several cases.

However, further research is needed to test the generalizability of the model across

different trade patterns.

1.5.3.4 Application of Predictive Analytics

Recent studies have demonstrated the potential of predictive analytics to

revolutionize trade forecasting. Balaji et al. (2018) harnessed machine learning techniques,

including LSTM, to predict the trade volume between China and its trading partners,

showcasing improved forecasting accuracy compared to traditional methods (Balaji et al.,

2018). Similarly, Dumor and Yao (2019) employed predictive analytics to anticipate trade

flows within the Belt and Road Initiative, elucidating the model's capacity to forecast trade

dynamics across diverse regions.

The evolution of trade forecasting techniques has led to a transition from traditional

methods to advanced predictive analytics, resulting in a shift towards more accurate,

dynamic, and insights-driven predictions. The integration of machine learning algorithms

and deep learning architectures such as LSTM presents a promising opportunity for trade

analysts to gain a deeper understanding of trade patterns and anticipate their evolution on

a global scale.

The integration of predictive analytics into economic forecasting has ushered in a

new era of insights-driven decision-making. This section delves into the transformative

role of predictive analytics in anticipating critical macroeconomic indicators, such as

GDP, inflation, and more. Moreover, it examines noteworthy studies that have harnessed

predictive analytics to forecast economic trends, shedding light on its implications for

informed policy formulation.

1.5.4 Role of Predictive Analytics in Economic Forecasting

Predictive analytics transcends traditional econometric approaches by leveraging

advanced algorithms and vast data repositories. By discerning intricate patterns within

historical and real-time data, predictive analytics augments the accuracy and timeliness of

economic predictions. Its application extends to pivotal macroeconomic indicators,

including Gross Domestic Product (GDP) growth, inflation rates, and unemployment

levels, thereby equipping policymakers and stakeholders with crucial foresight.

Table 2: Studies utilizing predictive analytics in economic forecasting

Study

Predictive Analytics

Approach

Economic

Indicators

Forecasted

Implications for

Policymaking

Mishkin (1998)

Neural Networks

Inflation

Enhanced inflation

prediction

Giannone et al.

(2008)

Dynamic Factor

Models

Industrial

Production

Improved industrial

production forecasting

Peersman, G.

(2004)

Bayesian VAR

GDP growth

Accurate GDP growth

forecasts

Koop et al.

(2019)

Mixed-Frequency

Models

Unemployment

Rate

Timely unemployment

rate forecasts

Barbaglia &

Manzan (2023)

Machine Learning

and Dynamic Factor

Models

Economic

Sentiment

Enhanced sentiment-

based economic

forecasting

The aforementioned studies shown in Table 2 collectively underscore the

efficacy of predictive analytics in economic forecasting. Estrella and Mishkin (1998)

demonstrated the viability of neural networks in enhancing inflation predictions (Estrella

& Mishkin, 1998). Giannone et al. (2008) showcased the utility of dynamic factor models

in refining industrial production forecasts. Peersman (2004) harnessed Bayesian VAR

models for accurate GDP growth predictions. Koop et al. (2019) employed mixed-

frequency models to achieve timely unemployment rate forecasts. Furthermore, Barbaglia

and Manzan (2023) illustrated the potential of machine learning and dynamic factor

models in sentiment-based economic forecasting.

In essence, predictive analytics empowers economic forecasters with an arsenal of

tools to unravel the intricacies of economic trends. By extracting meaningful insights from

vast and diverse datasets, predictive analytics enriches the accuracy, granularity, and

timeliness of economic predictions, ushering in a data-driven paradigm that holds

profound implications for policy-making and strategic decision-making.

1.5.5 Predictive Analytics in International Trade

International trade is one area where predictive analytics has found a strong

application due to its aptitude for identifying subtle patterns within large, complex

datasets. The studies in this section that have used predictive models to navigate the

complex world of cross-border trade delve into the specific uses of predictive analytics in

international trade. These applications include predicting trade effects in the face of

economic shocks, analyzing the effects of trade agreements, and anticipating changes in

trade patterns.

1.5.6 Anticipating Changes in Trade Patterns

Predictive analytics serves as a beacon for trade analysts seeking to forecast shifts

in trade patterns. In the work of Nabipour et al. (2020), machine learning models were

employed to predict export market diversification, enabling nations to foresee potential

shifts in their export destinations and strategize accordingly. By utilizing historical trade

data, these models reveal subtle changes in market preferences, enabling nations to

proactively adapt their trade strategies.

1.5.7 Analyzing Trade Agreement Impacts

Predictive analytics explores the complex nature of trade agreements and

illuminates their potential effects. Raza et al. (2016) utilized machine learning techniques

to analyze the impact of trade agreements on Nigeria's trade flows, providing insights into

the dynamics of trade after agreements. These analyses provide decision-makers with the

ability to evaluate the success of trade agreements and adjust their strategies to maximize

trade advantages.

1.5.8 Predicting Effects of Economic Shocks on Trade

Predictive analytics emerges as a shield against economic uncertainties by offering

predictions of trade effects amid shocks. The study by Glikson and Woolley (2020)

showcased how machine learning models can predict the effects of tariff changes on trade

flows, enabling governments and businesses to prepare for potential disruptions. These

predictive insights equip stakeholders with foresight, enabling agile responses to safeguard

trade interests. Table 3 shows the applications of predictive insights in the international

trade.

Table 3: Applications of predictive analytics in international trade

Study

Predictive Analytics

Approach

Application

Implications

Nabipour et al.

(2020)

Deep Learning

Models

Export Market

Diversification

Proactive adaptation

of trade strategies

Raza et al.

(2016)

Machine Learning

Techniques

Impact of Trade

Agreements

Informed policy-

making

Glikson &

Woolley

(2020))

Machine Learning

Models

Effects of Tariff

Changes on Trade

Flows

Preparedness for

economic disruptions

The highlighted studies collectively underscore the potency of predictive analytics

in international trade, unveiling its role in shaping strategic decisions in an era of global

economic intricacies. The capacity to anticipate trade pattern shifts, assess trade agreement

impacts, and predict trade effects amidst economic shocks lends an unparalleled edge to

stakeholders navigating the complex terrain of international trade.

1.5.9 Predictive Analytics in the UAE's Trade Landscape

The use of predictive analytics in the context of the United Arab Emirates (UAE)

trade landscape is a dynamic interplay of cutting-edge technology and economic foresight.

This section investigates studies that have explored the field of predictive analytics to shed

light on the complex dimensions of trade dynamics in the UAE. Researchers have

examined historical trade data, predicted future trade patterns, and improved the precision

of trade forecasts within the unique economic context of the UAE by using predictive

models. In Table 4, the exploration of predictive analysis of studies is shown regarding

UAE trade forecasting.

Table 4: Exploration of predictive analytics studies in UAE trade forecasting

Study

Predictive Analytics

Approach

Application

Key Findings

Worku & Rao

(2018)

Artificial Neural

Networks

Non-Oil Trade

Forecasting

Improved prediction

accuracy

Ismail et al.

(2020)

ARIMA Models and

Machine Learning

Techniques

Foreign Trade

Forecasting

Enhanced trade

forecasts for policy-

making

Sen & Dutta

(2022)

Long Short-Term

Memory (LSTM)

Model

Dubai's Non-Oil

Foreign Trade

Forecasting

LSTM-based model

outperforms baseline

Al-Mansoori

et al. (2018)

Artificial Neural

Networks

Non-Oil Trade

Forecasting

Improved prediction

accuracy

The studies presented here collectively underscore the transformative potential of

predictive analytics in elucidating the UAE's trade landscape. By leveraging predictive

models and data-driven insights, researchers have enriched the accuracy, granularity, and

timeliness of trade forecasts, thus enabling policymakers and stakeholders to navigate the

intricacies of the UAE's trade dynamics with a heightened degree of confidence.

1.5.10 Challenges and Limitations

While predictive analytics offers unprecedented potential to revolutionize trade

forecasting, it is not without its challenges and limitations. As researchers delve into this

dynamic realm, they must navigate a complex landscape marked by intricacies related to

data quality, model accuracy, interpretability, and potential biases. Awareness of these

challenges is crucial for conducting accurate and informed trade forecasts. Table 6

presents challenges and limitations in Predictive Analytics for Trade Forecasting.

1.5.10.1 Data Privacy and Security

To improve the accuracy and dependability of trade forecasting, in the UAE the use

of encryption is crucial for protecting economic data. This cryptographic technique

enables calculations to be performed on encrypted data allowing LSTM predictive models

to analyze and predict trade patterns without decrypting the information. As a result all

trade data, including imports, exports and detailed GDP components remains secure

throughout the analysis process guarding against access and potential data breaches. By

utilizing encryption the UAE ensures that its economic activities remain confidential while

benefiting from predictive analytics to inform trade decisions and strategies. This

strengthens its reputation as an forward thinking participant, in trade.

1.5.10.2 Data Quality and Availability

Predictive analytics heavily relies on robust, accurate, and comprehensive data.

However, trade data can be fragmented, inconsistent, and subject to reporting delays. Data

quality issues can undermine the efficacy of predictive models, leading to inaccurate

forecasts. Researchers must grapple with missing data, outliers, and discrepancies, which

can introduce noise and affect the model's performance.

1.5.10.3 Model Accuracy and Generalization

The accuracy of predictive models hinges on their ability to generalize beyond the

training data. Overfitting—an issue where models capture noise instead of patterns—can

compromise forecasting accuracy. Striking the right balance between model complexity

and simplicity is a delicate task, as overly complex models may perform well on training

data but falter in real-world scenarios.

1.5.10.4 Interpretability and Transparency

Sophisticated predictive models, such as deep learning architectures, can be

challenging to interpret. The "black-box" nature of some models limits their transparency,

making it difficult to discern the underlying factors driving forecasts. Researchers may

face resistance from stakeholders who demand comprehensible explanations for forecast

outcomes.

1.5.10.5 Potential Biases and Assumptions

Predictive models are not immune to biases that may exist in historical trade data.

Biases can arise from various sources, such as sampling methods, measurement errors, and

inherent data inequalities. Unaddressed biases can lead to skewed forecasts that fail to

capture the true trade dynamics.

1.5.10.6 Dynamic Nature of Trade

The trade landscape is highly dynamic, influenced by geopolitical shifts, economic

policies, and global events. Predictive models may struggle to adapt swiftly to unforeseen

changes, especially when these shifts deviate from historical patterns.

Table 5: Challenges and limitations in predictive analytics for trade forecasting

Challenge

Description

Implications

Data Quality and

Availability

Fragmented, inconsistent, and

delayed data can compromise

model performance

Inaccurate forecasts and

reduced confidence in

predictions

Model Accuracy and

Generalization

Overfitting and poor

generalization can impact

forecast reliability

Unreliable forecasts in real-

world scenarios

Interpretability and

Transparency

Complex models may lack

transparency and hinder

understanding

Resistance from stakeholders

and limited insights

Potential Biases and

Assumptions

Biases in historical data can lead

to skewed forecasts

Misrepresentation of trade

dynamics

Dynamic Nature of

Trade

Rapidly changing trade

landscape can challenge model

adaptation

Inability to capture

unforeseen changes

In navigating these challenges, researchers must adopt a holistic approach that

involves data preprocessing, careful model selection, validation techniques, and a deep

understanding of the trade context. Recognizing and addressing these limitations is pivotal

to harnessing the full potential of predictive analytics for trade forecasting, ultimately

enabling more accurate and informed decision-making.

Chapter 2: Methodology

In this study, we harnessed the predictive capabilities of the Long Short-Term

Memory (LSTM) algorithm, a specific type of recurrent neural network (RNN). LSTMs

excel in analyzing time-series data, a quality particularly valuable for forecasting trade

dynamics such as volumes and values, owing to their capacity to discern intricate temporal

relationships within sequential datasets.

2.1 Development of the LSTM Model

Our LSTM model was structured with careful consideration for the predictive

analysis: We have the following setup for our LSTM model:

2.1.1 First LSTM Layer (Input Layer)

The first LSTM layer is configured with 50 units, and it utilizes the Rectified Linear

Unit (RELU) activation function. This layer serves as the input layer and is responsible

for processing the sequential input data.

2.1.2 Second LSTM Layer

This layer also consists 50 units with RELU activation and returns sequences, it

distinct from the first layer as it is operated as an intermediary tier. This allows the model

to further capture more complex temporal patterns in the data.

2.1.3 Third LSTM Layer

Comprises 50 units with RELU activation but doesn't return sequences. This layer

aggregates the information learned from the previous layers into a condensed

representation.

2.1.4 Dense Layers

Two Dense layers follow the LSTM layers. The first Dense layer has 25 units, and

the second Dense layer contains a single unit (output layer). These Dense layers are fully

connected layers responsible for generating the final output of the model. They help

transform the LSTM layer's output into a prediction or forecast.

2.2 Optimization of the LSTM

To enhance the model's performance, we employed the Adam optimizer, known for

its efficiency in guiding deep learning models toward optimal minima. Concurrently, we

adopted the mean squared error (MSE) loss function, a particularly suitable choice for

LSTM-based forecasting tasks. The MSE quantifies the average squared deviation

between predicted values and actual observations, aligning with the essence of forecasting

precision.

2.3 Model Evaluation

The model evaluation phase involved assessing its predictive efficacy through

comparisons with previously unseen test data. This procedure guards against overfitting,

ensuring the model's generalization ability to novel data.

2.4 Hyperparameter Tuning

Hyperparameters, critical configuration settings of the LSTM model, underwent

meticulous fine-tuning. These included hidden unit count, layer depth, optimization

algorithm, and loss function. Multiple techniques, encompassing grid and random search,

were employed in the process.

2.5 Model Training

The training regimen of the model followed a systematic sequence:

2.5.1 Initialization

Commencing with random weight assignment, the model began its learning

journey. The complete steps for the training of LSTM model are shown in Figure 3.

2.5.2 Training Data Integration

The model was exposed to comprehensive training data, and its internal weights

were adjusted iteratively to minimize the loss function.

2.5.3 Evaluation

Subsequently, the model underwent evaluation using an independent test dataset,

with performance metrics recorded.

2.5.4 Iterative Refinement

Steps 2 and 3 were repeated iteratively until the model exhibited convergence or

until a predefined number of training iterations transpired.

Figure 3: Training flow chart of LSTM model

2.5.5 Performance Assessment

Model evaluation entailed a quantitative assessment, involving a comparison

between predicted values and actual values. The mean squared error (MSE) served as the

quantitative measure, with reduced MSE values indicative of heightened predictive

accuracy.

2.6 LSTM Model for Trade Analysis (Imports/Exports)

The trade analysis conducted for this study focused on the UAE's import and export

patterns with various nations to provide insights into the dynamics of global trade. A strong

data analysis tool called LSTM, a particular kind of recurrent neural network (RNN) was

used to accomplish this.

Following libraries were imported:

• pandas for data manipulation

• numpy for numerical computation

• matplotlib.pyplot and seaborn for data visualization

• sklearn.preprocessing for data preprocessing

• keras.models and keras.layers for building and training the LSTM

model

• os for accessing files and directories.

Our research framework benefited greatly from the use of the Pandas libraries,

which made it easier to manage the UAE's trade dataset and provide data for the LSTM

model training. Our study's key components—the resulting visualizations and LSTM

model training offered priceless insights into the trade dynamics and economic indicators

of the UAE.

2.7 Dataset Acquisition and Data Cleaning

In the data acquisition phase of this thesis, essential datasets for the analysis of trade

dynamics in the United Arab Emirates (UAE) and GDP calculations were gathered from

reliable sources. Import and export data for the UAE, as well as selected developed

countries, were collected from various websites offering comprehensive trade statistics.

Additionally, GDP figures for the UAE and the chosen developed nations were obtained,

serving as critical metrics to assess the economic impact of trade activities. The selection

of data sources was based on their credibility, relevance, and availability of historical data,

ensuring the robustness and accuracy of the analysis.

The data-cleaning process is a crucial and essential part of the research process. It

forms the basis for the accuracy and dependability of our LSTM model and subsequent

analyses. In this regard, the following fundamental data-cleaning procedures were

carefully carried out.

2.7.1 Row Deletion with NaN Values

In our dataset, we systematically removed any rows that contained NaN (Not-a-

Number) values. This step is crucial because it makes sure that none of the information in

our data is missing or incomplete. Our LSTM model relies on a large and coherent dataset,

so any omissions could seriously impair its ability to learn and predict.

2.7.2 Column Renaming

The process of renaming columns is not just a matter of nomenclature; it is a

deliberate attempt to improve the readability and comprehension of data. We can precisely

identify and comprehend the variables being taken into consideration thanks to descriptive

and well-defined column names. This step is important to take to make sure that our model

can accurately capture the subtleties of the data.

2.7.3 Streamlining Column Names

Column names can be made simpler to further streamline the data. Clear and

consistent column labels encourage uniformity and facilitate easy referencing throughout

the analysis. This helps to make our visualizations and results more understandable as well

as to train our LSTM model.

2.7.4 Elimination of Completely NaN Columns and Rows

Completely NaN columns and rows are redundant and don't provide any useful

information. Their presence can introduce noise to the data, which could result in incorrect

inferences. Therefore, getting rid of them is essential to maintaining data integrity.

2.7.5 Index Resetting

Resetting the index ensures that the data are organized coherently and sequentially.

This is necessary to preserve the dataset's temporal and relational characteristics, which

are especially important in time-series analysis, as is the case with our LSTM model.

Each of these data-cleaning procedures significantly raises the standard and

dependability of our research. They work together to strengthen our LSTM model's

accuracy and robustness, which strengthens the validity of our findings and conclusions.

This methodical approach highlights our dedication to scientific integrity and rigor in our

examination of the trade dynamics and economic indicators of the UAE.

2.7.6 Data Extracting

In this first step, we focus on extracting and isolating the data related to total exports

to a particular country. This is a key element of our analysis because it allows us to focus

on a particular aspect of trade dynamics. We can effectively train our LSTM model to

recognize and predict patterns in the export behavior of the UAE with respect to specific

countries by limiting our focus in this way.

2.7.7 Data Normalization

To standardize the data and bring it to a common scale, normalization is a crucial

preprocessing step. When working with numerical features that may have different ranges,

this is crucial. We ensure that all input variables are treated equally by the model by

normalizing the data. This step helps the LSTM remain stable and converge during training

by ensuring that no single variable has an overwhelming influence on the learning process.

2.7.8 Data Splitting

We divide our dataset for model evaluation, following best practices to prevent

overfitting. Initially, 70% of the data is allocated for training, and 30% for testing. From

the training data, we further allocate 90% for training, and the remaining 10% for

validation. This multi-tiered division allows us to gauge the model's performance on

unseen data effectively and independently validate its predictive capabilities.

2.8 Function of Dataset Creation for LSTM

For LSTM modeling, it is essential to create a suitable dataset structure. We create

a function that makes it easier to arrange our data into batches or sequences that are

appropriate for LSTM input. This function sets up the data into input-output pairs so that

the LSTM can successfully learn sequential patterns. The model can learn and predict

trends over time because each input sequence has a corresponding target or output

sequence.

2.9 Cryptographic Enhancement for Data Security

To ensure the security of the data we incorporated cutting edge methods. We

employed encryption on the datasets before conducting analysis allowing our LSTM

model to perform calculations, on encrypted data while keeping its confidentiality intact.

This encryption method guaranteed that our model could extract insights from datasets

ensuring the preservation of data privacy and integrity, throughout the forecasting process.

The seamless integration of cryptographic security within the LSTM's operational

framework set a new precedent for conducting sensitive economic analyses without

exposing the data to potential vulnerabilities.

2.9.1 Homomorphic Encryption

Homomorphic encryption is a cryptographic technique that allows calculations to

be conducted on encrypted data, producing an encrypted output that, when decrypted,

yields the same result as if the operations were performed on the original unencrypted data.

This implies that information can be encoded and transmitted to a third party for analysis

without ever revealing the original data. The third party has the capability to carry out the

required calculations and provide the outcomes in an encrypted format, which can only be

decrypted and understood by the original data owner. Homomorphic encryption enables

the secure analysis of confidential trade data in the realm of predictive analytics, especially

in sensitive areas such as trade forecasting. This ensures that companies and governments

can take advantage of advanced data analytics while maintaining the highest standards of

data privacy. In an age where data breaches pose a substantial threat, this tool serves as

a potent means of safeguarding data privacy. It facilitates secure calculations in the cloud

while keeping privacy.

2.10 LSTM Model Preparation and Prediction

A python code was used for the preparation and prediction of LSTM model. The

first line of code, train_size = int(len(scaled_data) * 0.7), defines the training set size as

70% of the total data. The next two lines of code, train, test = scaled_data[0:train_size, :],

scaled_data[train_size:len(scaled_data), :], split the data into the training set and the test

set.

The next five lines of code, scikit-learn's TimeSeriesSplit function to conduct time

series cross-validation. In this specific case, the code is configured to perform 10 splits.

Within a loop, the data is divided into training and testing subsets, preserving the temporal

order of the data. The 'train_index' and 'test_index' variables capture the indices of these

subsets. This approach enables robust model evaluation on time series data, providing a

series of train-test sets for assessing predictive accuracy across different time periods, and

likely stores the root mean squared error values in the 'rmse' list for each fold during cross-

validation.

The LSTM model can then be trained on the training set. The predictions of the

model can then be evaluated on the test set.

We have used Keras library to define and configure a neural network model. It

starts by importing necessary components, including LSTM and Dense layers, and the

Sequential model. A Sequential model represents a linear stack of layers, where data flows

sequentially from one layer to the next.

The model is initialized with the Sequential() function. It then incorporates three

LSTM layers, each containing 50 units, activated by the rectified linear unit (ReLU)

function. The use of return_sequences=True in the first two LSTM layers signifies that

these layers return the output as sequences, which is often necessary when stacking

multiple LSTM layers.

Following the LSTM layers, two Dense layers are added to the model. The first

Dense layer has 25 units, and the final layer has just one unit. Dense layers are fully

connected layers, and the last layer with one unit is commonly used for regression tasks.

The model is compiled using the mean squared error (MSE) loss function and the

Adam optimizer, which are standard choices for regression problems. Once the model is

configured, it can be used for training and making predictions on your data.

A model fitting code was employed for model training, particularly in the context

of deep learning using the Keras library. The training process involves iteratively

optimizing the model's parameters to learn patterns from the provided training data. The

training dataset consists of input data (xtrain) and the corresponding target outputs (ytrain)

that the model aims to predict. With 200 training epochs, the model will undergo multiple

passes through the training data, adjusting its weights to minimize the prediction error. A

batch size of 1 indicates that the model updates its parameters after processing each

individual data point, resembling stochastic gradient descent. The training process will

produce updates on the training progress (verbosity set to 1), and data shuffling is disabled,

meaning the order of the training data remains constant during training. Overall, this code

initiates the essential training phase, enabling the model to learn from the training dataset

and improve its predictive capabilities.

The code "ypred = model.predict(xtrain)" is a simple and crucial step in machine

learning. Here, a pre-trained model takes the input data, xtrain, and produces predictions,

which are stored in the variable ypred. These predictions are the model's estimates based

on the input data and can be used for various tasks such as making forecasts or

assessments.Similar to the previous line of code, this one makes predictions using the

trained LSTM model on the testing dataset (X_test). The X_test dataset serves as an

independent evaluation set and contains data that the model has not seen before. The

model's ability to generalize and produce precise forecasts for unforeseen data is evaluated

through predictions on this dataset.

To integrate cryptographic techniques with the predictive modeling in Python, we

would use encryption to secure the data before making predictions, and then decrypt the

predictions. This process would require a homomorphic encryption scheme that allows

computations to be performed on encrypted data. We need to import Pyfhel, PyPtxt,

PyCtxt from Pyfhel package. Here, is the encryption part code representation:

Initialize the Pyfhel object and HE = Pyfhel(), homomorphic encryption. Generate

key using HE.keyGen(). For the input data xtrain for the model in plaintext, the encrypted

data will be encrypted_xtrain = [HE.encryptFrac(x) for x in xtrain]. Here, the

`model.predict()` method would have to be adapted to work with encrypted data .The

encrypted test data using the public key is ‘xtest_encrypted’. Predictions with an LSTM

model that is equipped to handle encrypted data. This requires the model to be specifically

designed or adapted for working with homomorphic encryption libraries ypred_encrypted

= [model.predict(x) for x in xtest_encrypted]. Decrypt the results using the private key

'secret_key' would typically be held by the client who needs to see the results. ypred =

[y.decrypt(secret_key) for y in ypred_encrypted]. 'ypred' now contains the decrypted

predictions

Transforming normalized values into original scale is very important as it

transforms normalized values into their original scale. Input features and target variables

are frequently scaled to aid in model training and convergence. However, it's important to

deliver results in a format that is comprehensible and matches the units of the original data

when presenting the model's predictions to stakeholders or users. It is possible to present

the results in a way that is more significant and clearer by converting the predictions back

to their original scale. The working of this code snippet is given below.

Chapter 3: Experimentations and Results

3.1 Comparison of Imports and Exports

Comparing exports and imports is a crucial part of the analysis carried out in this

study. The bar graph shown in Figure 4 gives a clear and insightful depiction of the United

Arab Emirates' (UAE) trade relations with many important countries, illuminating export

and import values. Nine bars make up this graphic representation, each of which is

significant in relation to the UAE's international trade. The bars are set up in descending

order of value, with the shortest bar denoting the lowest trade value and the tallest bar

representing the highest.

*(All units are in Thousands USD)

Figure 4: Total trade values of imports and exports for countries and UAE

The United States is represented by the graph's tallest bar, making it the top export

destination for the UAE. This large bar emphasizes the substantial value of goods exported

from the UAE to the USA, highlighting the strong trade ties between these two countries.

We have another large bar at the opposite end that represents China as the UAE's main

importer. This bar highlights the sizeable amount of merchandise imported from China

into the United Arab Emirates, underscoring the crucial contribution China makes to the

UAE's domestic market. The value of imports from the USA is shown in the third bar. It

represents a sizable amount of American goods being imported into the UAE, even though

it is not as tall as the export bar to the USA.

Despite being shorter than the import from China, the fourth bar shows an important

amount of goods exported from the UAE to China. The UAE's ability to export to the

Chinese market is demonstrated by this. The value of goods exported from the UAE to the

UK is shown in the fifth bar. It displays the UAE's presence in the UK market despite not

being as tall as the top bars. The imports from the UK into the UAE are shown in the sixth

bar. It alludes to the quantity of merchandise arriving from the UK in the UAE. Even

though it is not as tall as some of the earlier bars, the seventh bar shows the amount of

imports from Canada into the UAE. The eighth bar gives information about trade relations

with India by showing imports from India into the UAE. The value of goods exported from

the UAE to Australia is represented by the ninth and shortest bar in the graph. Even though

it is the least valuable of the bars, it still shows trade connections with Australia.

3.2 Trade Imbalance

With China as a major import partner and the USA as a significant export

destination, there is a significant difference in the height of the bars between imports from

China and exports to the USA.

3.3 Diverse Trade Relationships

The graph highlights the UAE's diverse trade relationships, highlighting the

country's significant imports and exports to and from a number of nations, including the

USA, China, the UK, Canada, and India.

3.4 Trade Expansion Potential

Trade Growth prospects the export bars of the UK, Australia, and other countries

show areas where the UAE can expand and diversify its export markets.

The trends seen in the previous Figure are corroborated in Figure 5 below, which

compares the UAE's imports and exports with its top trading partners. This visualization

confirms important findings and offers additional insight into the trade dynamics of the

UAE. Figure 5 probably shows that the UAE consistently conducts business with its top

trading partners. This consistency in trading partners may signify some dependability and

mutual benefit in these economic ties. Figure 5 may represent the trade balance between

the UAE and its main trading partners similar to Figure 4. With certain nations, a recurring

trade surplus or deficit can have serious economic repercussions.

*(All units are in Thousands USD)

Figure 5: Comparison of imports and exports from UAE with top trade partners

The statistics may show how heavily dependent the UAE is on certain nations for

imports. Given how this dependence relates to supply chain resilience and diversification,

economic policymakers may want to take it into account strategically. The export bars can

be used to determine the main markets for UAE goods.

3.5 Heatmap Visualization for Trade Analysis

The trade relationships between the United Arab Emirates (UAE) and its main

trading partners, including the United States (USA), China, the United Kingdom (UK),

and Australia, are further examined and visualized in Figure 6, which is a heatmap

representation. As seen in Figure 6, each heatmap cell represents a distinct trade

relationship between the UAE and a specific nation. Each cell's color is graduated to show

the size of the trade values. Higher values are denoted by darker, more intense colors,

while lower values are denoted by lighter hues. This color scheme enables a quick

evaluation of trade intensity.

*(All units are in Thousands USD)

Figure 6: Heatmap of total exports and imports per country from UAE

The country that the UAE imports the most is indicated by the darkest cell in each

row, and the country that the UAE exports the most is indicated by the darkest cell in each

column. The information previously presented is visually reinforced by this comparison.

Trade imbalances can be more easily identified by looking at the heatmap. Trade dynamics

may alter over time. Researchers and decision-makers can spot changes in trade patterns

by contrasting the heatmap with earlier data or other visualizations. For instance, if a cell

in a recent heatmap that was previously lighter becomes darker, it denotes an increase in

trade volume with that country.

3.6 Correlation Matrix of UAE Trade Values

The correlation matrix can be used to identify nations with comparable trade

patterns. Predictions regarding the future trade values of various nations can be made using

this data. The correlation matrix in Figure 7 shows the correlation between the trade values

of various nations. The correlation coefficient determines the linear relationship between

two factors. The perfect positive correlation is indicated by a correlation coefficient of 1,

perfect negative correlation by a correlation coefficient of -1, and no correlation by a

correlation coefficient of 0. Several significant correlations between the trade values of

various countries have been discovered through analysis of the correlation matrix.

Figure 7: Correlation matrix of trade values

3.7 Trade Trend of UAE with Australia

Figure 8 shows the export values of products from the United Arab Emirates (UAE)

to Australia. It is the comparison of the products that are top traded in 2021. The top 10

products by export value in 2021 are shown in Table 6 below:

Table 6: Comparison of top products in 2021 with their trade values in 2005

Product Code

2005

2021

'74

189

337507

'71

6515

145651

'39

25783

85667

'73

9950

77243

'85

936

66356

'84

4272

52502

'87

1280

36318

'72

278

32664

'70

11373

30515

'33

4703

23513

*(All units are in Thousands USD)

The figure shows that the export values of the UAE to Australia have been

increasing over the past few years. This is likely due to the strong economic growth in

both countries. The UAE is a major oil exporter, and Australia is a major importer of oil.

The UAE is also a major exporter of aluminum, plastics, and other manufactured goods,

which are also in high demand in Australia. The fluctuations in the export values of the

UAE to Australia can be due to several factors, such as changes in the global economy,

changes in the political climate, and changes in the demand for specific products. For

example, the decline in the export value of mineral fuels in 2020 can be attributed to the

COVID-19 pandemic, which caused a global economic slowdown.

Figure 8 shows that the export values of the UAE to Australia are strong and are

likely to continue to grow in the future. This is good news for both countries, as it will

help to boost their economies and create jobs. Figure 8 shows the top 21 products traded

with Australia. It shows the comparison of the trade values of these products in 2005. The

huge spikes can be seen in the figure that shows the growing trend of trade between

Australia and UAE. On the X-axis, HS code of the product is shown and on the Y-axis,

the value of the trade is shown.

*(All units are in Thousands USD)

Figure 8: Top 10 Products traded between UAE and Australia in 2021 comparing with

2005

In Figure 9, a line graph of the trade value between the UAE and Australia is shown.

The trend line is a line that best fits the data points. It can be used to predict the future

value of the trade value. The trend line also shows that the rate of increase is slowing

down. This is likely due to numerous factors, including the global economic slowdown

and the decline in oil prices. Despite the slowdown, the trend line indicates that the trade

value between the UAE and Australia will continue to increase in the future. This is

because the two countries have strong economic relations and there is a growing demand

for goods and services from them.

*(All units are in Thousands USD)

Figure 9: Trade value of Australia with UAE

3.8 Trade Trend of UAE with USA

Figure 10 shows the export trend line from trade between the United Arab Emirates

and the United States of America. The line represents the average value of the trade

between the United Arab Emirates and the United States of America.

*(All units are in Thousands USD)

Figure 10: Trade trend of UAE-USA between 2005 and 2007

The graph shows that the value of exports from the UAE to the USA has increased

steadily over time. In 2005, the value of exports was around $25 billion. By 2007, the

value of exports had increased to around $75 billion. The graph also shows that the value

of exports from the UAE to the USA has been relatively stable since 2007. There have

been some fluctuations in the value of exports from year to year, but the overall trend has

been one of stability.

*(All units are in Thousands USD)

Figure 11: Trade trend of UAE-USA between 2007 and 2008

The graph shown in Figure 11 is a scatter plot of the trade between the United Arab

Emirates and the United States of America, with the value of exports from the UAE to the

USA in 2008 on the y-axis and the value of exports from the UAE to the USA in 2007 on

the x-axis. The graph also shows a linear trendline, which is a line that best fits the data

points.

The trendline shows that there is a positive correlation between the value of exports

from the UAE to the USA in 2007 and the value of exports from the UAE to the USA in

2008. This means that the higher the value of exports from the UAE to the USA in 2007,

the higher the value of exports from the UAE to the USA in 2008 was. The trendline also

shows that the value of exports from the UAE to the USA increased from 2007 to 2008.

This is because the trendline is above the line y = x. The R-squared value of the trendline

is 0.98. This means that the trendline explains 98% of the variation in the data. This is a

very strong correlation, which suggests that the trendline is a good fit for the data. The

graph shown in Figure 11 depicts that there is a strong positive correlation between the

value of exports from the UAE to the USA in 2007 and the value of exports from the UAE

to the USA in 2008. The graph also shows that the value of exports from the UAE to the

USA increased from 2007 to 2008.

*(All units are in Thousands USD)

Figure 12: Trade trend of UAE-USA between 2008 and 2012

The graph shown in Figure 12 is a scatter plot of the trade between the United Arab

Emirates and the United States of America, with the value of exports from the UAE to the

USA in 2012 on the y-axis and the value of exports from the UAE to the USA in 2008 on

the x-axis. The graph also shows a linear trendline, which is a line that best fits the data

points.

The trendline shows that there is a positive correlation between the value of exports

from the UAE to the USA in 2008 and the value of exports from the UAE to the USA in

2012. This means that the higher the value of exports from the UAE to the USA in 2008,

the higher the value of exports from the UAE to the USA in 2012 was.

*(All units are in Thousands USD)

Figure 13: Trade trend of UAE-USA between 2012 and 2013

Figure 13 consists of a graph that shows a scatter plot of the export trend line for

trade between the United Arab Emirates and the United States of America in 2012 and

2013. The x-axis shows the value of exports in 2012 and the y-axis shows the value of

exports in 2013. The trendline shows the average value of exports from the UAE to the

USA between 2012 and 2013.

*(All units are in Thousands USD)

Figure 14: Trade trend of UAE-USA between 2018 and 2019

Figure 14 shows the graph of exports from the UAE to the USA that decreased

slightly from $75 billion in 2018 to $73 billion in 2019. This represents a decrease of

approximately 3%.

The decline in exports from the UAE to the USA in 2019 is likely due to several

factors, including:

• The global economic slowdown.

• The decline in oil prices.

• The trade war between the US and China.

*(All units are in Thousands USD)

Figure 15: Trade trend of UAE-USA between 2019 and 2020

The trading trend shown in Figure 15 between the UAE and the USA from 2019

to 2020 was also slightly negative similar to the trend between 2018 and 2019. The value

of exports from the UAE to the USA decreased slightly by approximately 3%.

The decline in exports from the UAE to the USA in 2020 is likely due to several

factors, including:

• The global economic recession caused by the COVID-19 pandemic.

• The decline in oil prices.

• The trade war between the US and China.

*(All units are in Thousands USD)

Figure 16: Trade trend of UAE-USA between 2020 and 2021

The trade between the USA and the UAE has been increasing. The United States

exported $14.9 billion to the UAE and imported $5.6 billion from the UAE in 2021, for a

total bilateral trade value of $20.5 billion. This is an increase from the previous year of

16%. Transportation equipment, machinery and mechanical appliances, and electrical

machinery and equipment are the major US exports to the UAE. Petroleum goods,

aluminum, and precious stones are the main UAE exports to the USA.

The UAE is the United States' greatest trading partner in the Middle East and its

19th-largest trading partner overall. After China, the US is the UAE's second-largest

trading partner worldwide. The trade trend shown in Figure 16 between the UAE and the

USA is expected to continue to grow in the coming years, as both countries are committed

to strengthening their economic relations. The UAE is also investing heavily in expanding

its economy, which is creating new opportunities for US businesses.

3.9 Import Trade of UAE from USA

Figure 17 illustrates the import trade dynamics between the United Arab Emirates

(UAE) and the United States of America (USA) spanning from 2005 to 2021. It portrays

a clear picture of how the value of imports has evolved during this period in the Figure 17,

the x-axis denotes the years, and the y-axis denotes the import values in billions of US

dollars.

The trendline in the graph shows a general upward trajectory, signaling that the

value of imports from the USA to the UAE has predominantly been on the rise, although

with some random variations. Particularly, the total value of imports from the USA

exhibited significant growth, surging from $10.3 billion in 2005 to a significant $20.1

billion by 2021.

This gradual rise in imports can be attributed to several key factors. For example,

the USA and the UAE share a robust economic partnership. The UAE, endowed with

substantial oil and gas resources, complements the USA's technological and

manufacturing prowess. This economic cooperation gives strength to trade between the

two nations.

The UAE is actively pursuing economic diversification as part of its strategy to

reduce its dependency on oil and gas exports. One facet of this diversification strategy

involves increasing the importation of goods and services from other countries, including

the USA.

Another aspect is that the UAE's strategic geographical location as a gateway to

both the Middle East and Africa enhances its attractiveness as a prime destination for

imports from the USA. This strategic position facilitates trade flows and strengthens the

UAE's role as a key trading hub in the region.

Figure 17: Import trade between UAE-USA from 2005 to 2021

UAE and USA have strong relations with each other and their trade is increasing

day by day with a positive trend line as shown in Figure 17. Figure 18 shows all products

traded between UAE and USA from 2005 to 2021. This bar plot shows the trade value of

every HS product between these two countries.

*(All units are in Thousands USD)

Figure 18: All HS product’s import trade from 2005 to 2021

The import trade pattern between UAE and USA changed over time. The top 10

products in import trade between UAE and USA are shown in Table 7. The Following HS

Products remained the top products in the year 2021.

• '84: Nuclear Reactors, Boilers, Machinery, and Mechanical Appliances

• '87: Vehicles other than Railway or Tramway Rolling Stock, and Parts and

Accessories thereof.

• '88: Aircraft, Spacecraft, and Parts thereof.

• '85: Electrical Machinery and Equipment, and Parts thereof; Sound Recorders

and Reproducers, and Parts and Accessories of such Articles.

• '71: Natural or Cultured Pearls, Precious or Semi-Precious Stones, Precious

Metals, Metals Clad with Precious Metal, and Articles thereof; Imitation

Jewelry; Coin.

• '30: Pharmaceutical Products.

• '90: Optical, Photographic, Cinematographic, Measuring, Checking, Precision,

Medical or Surgical Instruments and Apparatus; Clocks and Watches; Musical

Instruments; Parts and Accessories thereof.

• '08: Edible Fruit and Nuts; Peel of Citrus Fruit or Melons.

• '39: Plastics and Articles thereof.

• '38: Miscellaneous Chemical Products.

Table 7: Top 10 products imported from UAE in 2021 by USA

Product Code

2021

'84

5080255

'87

2281896

'88

1247715

'85

1179753

'71

997520

'30

933831

'90

834848

'08

472115

'39

385738

'38

373496

*(All units are in Thousands USD)

3.10 LSTM Predictions

A Python script was developed to create a visual comparison between the predicted

and actual values for the dataset. The code proceeds the following steps:

• To begin, a figure of size 15 x 15 inches is generated using fig =

plt.figure(figsize=(15, 15)). This sets the dimensions for the visual representation.

• The figure is then divided into two subplots, creating a side-by-side arrangement

for comparison. Subplot ax1 is defined as fig.add_subplot(221), and ax2 is defined

as fig.add_subplot(222).

• In ax1, the script employs ax1.plot(ypred, 'r', label="Predictions") to plot the

predicted values in red ('r') with the label "Predictions." This subplot is dedicated

to visualizing the model's predictions.

• On the other hand, ax2 is dedicated to displaying the actual values. Using

ax2.plot(ytrain, 'b', label="Actual"), the actual values are plotted in blue ('b') with

the label "Actual."

• For both subplots, labels are set for the x-axis and y-axis, where

ax1.set_xlabel("Time") and ax1.set_ylabel("Value") are used to label the predicted

values, while ax2.set_xlabel("Time") and ax2.set_ylabel("Value") label the actual

values. Additionally, titles are assigned to each subplot: "Predicted Values" for ax1

and "Actual Values" for ax2.

• The plt.show() function is then called to display the figure, presenting a visual

comparison of predicted and actual values over time.

This script and code arrangement provide a clear visual representation, enabling an

easy comparison between the model's predictions and the actual values in the dataset.

The implementation of this code played a pivotal role in visually assessing the

model's performance against the training data, a critical step in the data analysis process.

Below, we will present graphical representations, showcasing predictive curves alongside

the corresponding actual ones for both imports and exports between the UAE and other

countries, as well as the UAE's imports and exports on a global scale.

3.10.1 UAE Exports to Australia

As shown in Figure 19, looking into the actual values, observed trend in the data is

notably smooth, with export values remaining relatively consistent until 2015. In 2016,

there was a discernible increase in exports, but the overall trend remained stable. This

consistency in the dataset's trend provides an advantageous foundation for the LSTM

model to comprehend and make accurate forecasts. Evaluating of the LSTM Model's

forecast, the Mean Squared Error (MSE) for this model is impressively low at 0.0065,

signifying a high level of reliability for forecasting. An MSE value below 1 is considered

satisfactory, underscoring the model's dependability.

Evidently, the model exhibits an exceptional capacity to adeptly learn from the data

and provide precise forecasts. Notably, this proficiency is demonstrated even with a

relatively small dataset, affirming the model's effectiveness in grasping intricate patterns

and making accurate predictions.

*(All units are in Thousands USD)

Figure 19: UAE exports to Australia - predicted vs. actual values

3.10.2 UAE Exports to China

In this analysis, we will delve into the forecasting of exports from the UAE to

China, spanning the years 2003 to 2022. It's noteworthy that the model architecture and

hyperparameters remain consistent throughout, underscoring the efficacy of our finely

tuned model in grasping intricate patterns from diverse trend datasets. Figure 20 shows the

actual vs. predicted values depicting the real export trajectory to China.

*(All units are in Thousands USD)

Figure 20: UAE exports to China - predicted vs. actual values

The model undergoes training on this dataset, achieving a commendable Mean

Squared Error (MSE) loss value of 0.0021, indicative of its precise performance, bearing

testament to our model's robust performance, resulting in accurate forecasting over a

dataset characterized by varying trends in values.

3.10.3 UAE Exports to UK

In our effort to forecast product exports to the UK, our model has been diligently

fine-tuned to provide accurate predictions while capturing the inherent complexities of the

dataset.

*(All units are in Thousands USD)

Figure 21: UAE exports to UK - predicted vs. actual values

As shown in Figure 21, the observed trend is characterized by significant variations

with numerous peaks and valleys, necessitating a complex model to effectively capture

these intricate features.

The Mean Squared Error (MSE) value is calculated at 0.3650, indicating that while

the model maintains good accuracy, it faces greater challenges due to the more intricate

trends in the data. Nevertheless, these results are deemed satisfactory for a general-purpose

model.

3.10.4 UAE Exports to USA

Upon training, the model successfully achieves a Mean Squared Error (MSE) loss

value of 0.4305. While this performance is commendable, it's noteworthy that achieving a

near-zero loss value (around 0.00) would indicate even higher accuracy. However, given

our model's fine-tuning to accommodate various datasets, it sometimes achieves

exceptionally high accuracy and at other times reaches a level of performance just shy of

perfect. It's important to maintain a balance as a reliable model should forecast values with

a loss less than 1. Figure 22 presents a comparison between actual and predicted forecasts.

*(All units are in Thousands USD)

Figure 22: UAE exports to USA - predicted vs. actual values

3.10.5 UAE Exports to Canada

In the case of exports to Canada, we encountered a substantial number of null

entries in most product categories. Considering the reduced dataset size, we adjusted the

folds split to 5.

Notably, the export trend to Canada exhibits a unique complexity. While time series

data often follows discernible trends, this dataset presents a less defined pattern, further

complicated by a smaller number of data points. Despite these challenges, our LSTM

model, which has been meticulously fine-tuned, manages to achieve a highly satisfactory

Mean Squared Error (MSE) loss value of 0.1543. This exceptional result lends credibility

to our model's forecasting capabilities. The forecasted result shown in Figure 23.

*(All units are in Thousands USD)

Figure 23: UAE exports to Canada - predicted vs. actual values

3.10.6 UAE Exports to the World

Below in Figure 24, the key difference to notice is that the trend in the data is quite

clear and straightforward, but the values are exceptionally high, around 3,500,000. The

model's loss, which is a measure of accuracy, is approximately 1. This higher loss is

primarily because of the challenge posed by dealing with these high, unstandardized

values, resulting in a less typical learning pattern.

*(All units are in Thousands USD)

Figure 24: UAE exports to the world - predicted vs. actual values

While the model effectively captures the general trend, it does struggle with these

high values. For instance, the highest predicted value is 85,000, while the actual values

can reach up to 3,500,000. Even though this presents a significant difference, our well-

tuned model still performs satisfactorily. This underscores the model's robustness and

ability to handle a variety of scenarios. Additionally, we conducted experiments by making

slight adjustments to the model's settings, including increasing the training epochs to 500,

aiming to enhance its capability to learn complex patterns.

3.10.7 Canada Imports to UAE

The import dataset presents a highly intricate trend as seen in Figure 25. Notably,

there are recurring cycles of rises and falls in imports, with a significant peak observed in

2020, likely attributed to the impact of COVID-19. It's important to emphasize that our

model excels at capturing the overall trend in the dataset but may struggle to identify and

adapt to unusual or anomalous trends. The loss resulting from model training is calculated

at 0.2882. Below, we present the actual and predicted forecasts.

*(All units are in Thousands USD)

Figure 25: Canada imports to the UAE - predicted vs. actual values

As expected, our model effectively learns and replicates the overall trend. However,

when faced with anomalies or unusual trends in the dataset, it may falter, resulting in

inaccuracies in its forecasts. Nonetheless, the model consistently provides a highly

accurate forecast of the general trend, maintaining a minimal loss and faithfully tracking

the overall import trend.

3.10.8 China Imports to the UAE

The model achieved a notably lower loss, approximately 0.0094. This suggests that

the model accurately captures the features and patterns within the dataset, and there are no

anomalies or unusual behaviors in the trend. This straightforward trend makes it easier for

the model to learn and make reliable forecasts. The actual and predicted forecasts are

presented Figure 26.

*(All units are in Thousands USD)

Figure 26: China imports to the UAE - predicted vs. actual values

As shown in the Figure 26, the model has successfully learned even subtle trends

within the dataset, providing highly accurate forecasts. This reliability is attributed to the

absence of anomalies in the trend data. The primary difference observed is that the peak

values in the actual dataset reach 1,400, while the model's predictions peak at 1,200. This

variation, although minor, is satisfactory and does not indicate any major deviations or

incorrect trends, further highlighting the effectiveness of our model.

3.10.9 India Imports to the UAE

Analyzing the import trend for India's total imports reveals a highly intricate

pattern, presenting a challenge for the model in capturing these complex features.

The observed trend is characterized by abrupt rises and falls, lacking the

smoothness of some other datasets. Despite this complexity, our model achieves a very

satisfactory loss, approximately 0.7625. This value, falling between the ranges of very low

and above 1, indicates that the model performs effectively in grasping the dataset's features

without either underfitting or overfitting.

In Figure 27 above, it's evident that the model excels at learning these intricate

features with impressive performance. The overall trend of rises and falls is accurately

mirrored by the model. The primary distinction lies in the peak values, where the actual

dataset reaches 60,000, while the model's forecast peaks at 47,500. Additionally, the model

may not accurately predict declines. However, when considering the entire forecast, it

remains highly satisfactory and reliable in capturing the complex import trend.

*(All units are in Thousands USD)

Figure 27: India imports to the UAE - predicted vs. actual values

3.10.10 UK Imports to the UAE

As shown in Figure 28, for the evaluation of imports from the UK, this dataset lacks

complex patterns or trends that would pose a significant challenge for the model's learning

and forecasting capabilities. However, it's noteworthy that the model registers a somewhat

higher loss, measuring 0.8129. This suggests that certain patterns or anomalies may be

present in the data that are unusual for the model. Given that this is a highly tuned model,

it is expected to capture the overall trend with a high degree of accuracy, and small

anomalies in the forecast are acceptable.

As previously discussed, the model effectively captures the general trend.

However, since there is a consistent rise in the trend, the model may struggle to learn these

complex features. Nevertheless, the overall trend of the forecast remains accurate,

indicating when imports are expected to increase or decrease. It's important to recognize

that LSTM models excel at learning from time series data, and our dataset, with around

10-20 entries, does present limitations in terms of learning. If we had a larger dataset,

regardless of its complexity or unusual trends, the model would provide very accurate

results. However, even with limited data points and a single model trained for all imports

and exports, the results remain highly satisfactory.

*(All units are in Thousands USD)

Figure 28: UK imports to the UAE - predicted vs. actual values

3.10.11 USA Imports to the UAE

In the evaluation of imports in the USA, the trend for the USA reveals a fairly

steady pattern until 2014 when there was a notable increase in imports, followed by a

subsequent decrease. Such a typical behavior can sometimes challenge the model's

learning capacity unless it's finely tuned. Fortunately, in our case, we have meticulously

fine-tuned our model to effectively learn complex features and deliver strong performance.

The loss achieved by the model stands at 0.8117, which is a commendable value, as it falls

below 1. The predicted and actual forecasts are presented in Figure 29.

*(All units are in Thousands USD)

Figure 29: USA imports to the UAE – predicted vs. actual values

It’s evident that the model encounters difficulties when there is an unusual increase

in imports. Nevertheless, it manages to capture this trend with good performance overall.

Additionally, when dealing with a dataset of limited size, a single abnormal rise can pose

challenges for the model’s learning process. However, our model consistently

demonstrates strong performance, effectively proving its reliability and effectiveness

across various imports and exports datasets.

3.10.12 The World’s Imports to the UAE

The import data was also analyzed for worldwide imports. The actual data shows a

complex pattern, with yearly variations and exceptionally high values, making it a

challenging dataset for the model to learn from. This complexity results in a loss value of

1, indicating difficulties in prediction. Predicted values display a similar trend after

optimization with the help of cross validation. LSTM models excel when data has a strong

and consistent trend, and the model's suboptimal performance is attributed to the dataset's

irregular behavior without a clear trend. Despite these challenges and the dataset's

anomalies, the model's overall performance remains satisfactory.

*(All units are in Thousands USD)

Figure 30: The world’s imports to the UAE – predicted vs. actual values

3.11 GDP Calculation Based on Imports and Exports

Gross Domestic Product (GDP) is the total value of all goods and services produced

in a country during a given period of time. It is one of the most important economic

indicators, as it provides a measure of the size and growth of an economy.

One way to calculate GDP is to use the expenditure approach. This approach

calculates GDP by adding up the following four components:

• Consumer spending.

• Business investment.

• Government spending.

• Net exports.

Net exports are the difference between a country's exports and imports. If a country

exports more goods and services than it imports, it has a trade surplus. If a country imports

more goods and services than it exports, it has a trade deficit.

The following formula shows how to calculate GDP using the expenditure

approach:

GDP = C + I + G + (X - M) Equation (4)

where:

C is consumer spending

I is business investment

G is government spending

X is exports

M is imports

Net exports are an important component of GDP because they represent the

contribution of foreign investment to the domestic economy. When a country exports

goods and services, it is essentially selling its production capacity to other countries. This

investment helps to create jobs and boost economic growth.

We calculate GDP with above mentioned formula in Equation No 4. We used

python libraries to calculate the GDP.

df['C'] is the column in the DataFrame that contains consumer spending.

df['I'] is the column in the DataFrame that contains business investment.

df['G'] is the column in the DataFrame that contains government spending.

df['X'] is the column in the DataFrame that contains exports.

df['M'] is the column in the DataFrame that contains imports.

The used Python code implements the formula by adding up the corresponding

columns in the DataFrame df. The result is a new column in the DataFrame df called

GDP, which contains the GDP for each year. Figure 31 shows the GDP calculated by the

above-mentioned equation from 2001 to 2020.

*(All units are in Billion USD)

Figure 31: GDP from 2001 to 2020

We trained an LSTM model to predict the GDP on the basis of the formula

discussed above.

The create_sequences() function creates sequences of data that can be used as input

to the LSTM model. The function takes two arguments: the data and the number of time

steps per sequence. The function iterates over the data, creating a sequence for each time

step. The sequence for each time step is a list of the next time_steps values in the data. The

function returns a NumPy array of sequences and a NumPy array of targets.

100

120

140

160

180

200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

GDP

The LSTM model is built using the Sequential model from Keras. The model has

two layers: an LSTM layer and a dense layer. The LSTM layer has 50 units and the dense

layer has 1 unit. The LSTM layer is activated using the relu function. The dense layer is

activated using the linear function. The model is compiled using the Adam optimizer and

the mean squared error loss function.

The model is trained using the fit() method. The fit() method takes four arguments:

the training data, the training targets, the number of epochs, and the batch size. The model

is trained for 500 epochs with a batch size of 32. A validation split of 20% is used. The

early stopping callback is used to stop training if the validation loss does not improve for

10 epochs. The reduced learning rate callback is used to reduce the learning rate if the

validation loss does not improve for 5 epochs.

The provided code creates and trains an LSTM model to predict future values of a

time series. The model can be used to predict future GDP, stock prices, or other time series

data.

Figure 32 shows the training loss vs validation loss curve of LSTM model. The

figure shows that the model converges after 260 epochs and here model early stops as we

used early stopping mechanism in our python code. It early stops as the loss stops

decreasing for ten consecutive iterations.

Figure 32: Training vs. validation loss curve of LSTM model

MSE loss is used in LSTM model for the evaluation of the model. Figure 32 shows

the decreasing MSE loss on the independent data.

3.12 Model Performance Evaluation

We used Mean Squared Error (MSE), and Correlation matrices as evaluation

matrices of our model. As seen previously in Figure 17, a comprehensive picture of the

correlation coefficient between different trade values. If the correlation is high (close to

1), it indicates a strong linear relationship between the predictions and the actual values,

suggesting that the model is doing a good job of capturing the linear trend in the data.

The training vs validation loss in Figure 32 shows the MSE loss values of training

and validation. The mean squared error (MSE) loss function is used as a metric for LSTM

model. It measures the average squared difference between the actual and predicted values.

Lower MSE indicates a better model. Figure 32 clearly shows that the model after 200th

iteration converges and the minimum difference between training MSE and validation

MSE shows the significance of the model.

Chapter 4: Discussions on Results

Results obtained from the LSTM model and visualization show that the trade of

UAE is increasing rapidly despite global change and other factors. Figure 33 to 37 shows

the trend analysis of each year with its previous year. It is UAE’s overall trade.

*(All units are in Thousands USD)

Figure 33: Trade trend between the year 2005 and 2007

The data points in Figure 33 in the graph are connected by a line, which indicates

that the trend is linear. The line has a positive slope, which means that the value of trade

is increasing. The line is not perfectly straight, which means that there is some variation

in the data.

*(All units are in Thousands USD)

Figure 34: Trade trend between the year 2013 and 2014

Although the rate of growth is not particularly rapid during this time, the trade

trend observed in the United Arab Emirates (UAE) between 2013 and 2014 indicates a

positive outcome as shown in Figure 34. This judgment is based on an analysis of trade

data, which indicates that while trade activity is on the rise, it is not experiencing

explosive or unexpected growth.

No significant increase in data points is seen between the years 2017 and 2018, as

shown in Figure 35. Particularly when compared to the years before, this suggests that

trade has no big increase during these years.

*(All units are in Thousands USD)

Figure 35: Trade trend between the year 2017 and 2018

*(All units are in Thousands USD)

Figure 36: Trade trend between the year 2019 and 2020

As shown in Figure 36, the trade trend between 2019 and 2020 in the UAE resulted

in a decline in both exports and imports as shown in Figure 36. Exports decreased by

11.7% from 314.5 billion in 2019 to 273.7 billion in 2020. Imports decreased by 10.3%

from AED 280.2 billion in 2019 to AED 251.1 billion in 2020. This decline could be due

to several factors, such as the COVID-19 pandemic, which caused a global economic

slowdown and a decline in demand for goods and services. The decline in oil prices

reduced the UAE's export earnings.

*(All units are in Thousands USD)

Figure 37: Trade trend between the year 2020 and 2021

The trade pattern of UAE's exports and imports increased between 2020 and 2021,

according to the trade pattern observed in Figure 37. From AED 273.7 billion in 2020 to

AED 300.9 billion in 2021, exports increased by 10%. From AED 251.1 billion to AED

284.2 billion in 2021, imports rose by 13.4%. Several factors contributed to this increase:

the COVID-19 pandemic-related slow global economic growth. The increase in oil prices,

which increased export revenue for the UAE, led to the increase in oil prices. The UAE's

efforts to reduce its dependence on oil exports and diversify its economy.

4.1 Yearly Product Wise Trade Analysis

In this section, we present the analysis of all the products traded in the UAE with

different countries. The Harmonized System (HS) nomenclature of products is used. The

HS is an international system of labeling goods for trade purposes. It is used by customs

authorities worldwide to collect import and export duties and to monitor trade flows.

*(All units are in Thousands USD)

Figure 38: Trade value of different products in UAE

UAE traded the HS-71 product as a top traded item with the USA as shown in

Figure 38. HS-71 is a product that consists of natural or cultured pearls, precious or semi-

precious stones, precious metals, metals clad. The second highest product traded in 2012

in UAE is HS-85 and HS-90 that is electrical machinery and equipment and parts thereof;

sound recorders and reproducers, television and optical, photographic, cinematographic,

measuring, checking, precision, medical or surgical.

Comparing 2012 with 2015 the trade has changed in UAE as in 2015 there was a

huge change in trade values with the USA as shown in Figure 39. UAE trade values in

different products increased significantly. These products include HS-71, HS-76, HS-88,

and HS-84, which are “Natural or cultured pearls, precious or semi-precious stones,

precious metals, metals clad ...”, “Aluminium and articles thereof”, “Aircraft, spacecraft,

and parts thereof”, and “Nuclear reactors, boilers, machinery, and mechanical appliances;

parts thereof”, respectively.

*(All units are in Thousands USD)

Figure 39: Trade value analysis of different products for 2015

Comparing these statistics with the graphs for the years 2020 and 2021, there is a

huge difference in trade patterns in the UAE and the USA as shown in Figure 40 and

Figure 41 respectively. Trade volume between these two nations increased significantly.

The signing of the US-UAE Comprehensive Strategic Partnership Agreement in 2020,

which is also expected to boost trade and investment between the two countries.

*(All units are in Thousands USD)

Figure 40: Trade value analysis of different products for 2020

*(All units are in Thousands USD)

Figure 41: Trade value analysis of different products for 2021

4.2 Trade Trends with USA, UK, China, India and Australia

A comprehensive overview of the trade analysis conducted for the UAE is

presented in Figure 42. It examines the dynamics of import and export between the UAE

and its major trading partners, including the USA, China, UK, India, and Australia. As it

provides a visual representation of both imports and exports, this figure is a useful tool for

deficit analysis. The figure presents fascinating insights into trade relations between the

UAE and these important countries. When analyzing the UAE's trade relations with the

USA, exports to the USA far outweigh imports from that country. The dynamics of trade

with China, however, show that there are significantly more imports from China than

exports to China.

*(All units are in Thousands USD)

Figure 42: Import and export analysis of UAE with different countries

Figure 42 captures a delicate depiction of the UAE's trade patterns, highlighting the

interaction of imports and exports with important countries and opening the door for more

thorough investigations in the field of international trade economics.

4.3 UAE Trade Analysis Under Changing Scenarios

The UAE is a global trade hub, and its trade patterns are influenced by a variety of

factors, including economic conditions, government policies, technological changes, trade

agreements, and exchange rates.

In recent years, the UAE has been working to diversify its economy and reduce its

reliance on oil exports. This has led to an increase in non-oil exports, such as manufactured

goods and services. The UAE has also become a major re-export hub.

The UAE's trade is also influenced by its strategic location. The UAE is located at

the crossroads of Europe, Asia, and Africa, and it has a modern and efficient transportation

and logistics infrastructure. This makes the UAE an ideal location for businesses that want

to trade with other countries in the region.

In this section, we analyze some of the scenarios and their implications and effects

on UAE trade.

4.4 Time to Import Pre-COVID and Post-COVID

Before the COVID-19 pandemic, importing goods into the UAE used to take about

7-10 days on average that is slightly changed after COVID-19. Nowadays, it typically

takes around 10-14 days. There's been a surge in the UAE's economy after the worst of the

COVID-19 situation passed. People are buying more things from other countries, which

means there's a lot more stuff coming in. This has made the whole process of bringing in

goods slower because there's just so much more to handle.

The way goods move around the world got messed up by COVID-19. Ships and

planes couldn't move as freely, and that caused delays. So, even if there's a strong demand

for stuff in the UAE, it's taking longer for things to arrive.

*(All units are in Thousands USD)

Figure 43: Pre and Post-COVID average time to import

The UAE government is taking extra precautions to stop COVID-19 from

spreading. This includes checking things carefully at ports, which, although necessary,

slows things down.

Despite these delays, the UAE is still a big player in global trade. The government

is determined to make importing stuff smoother and faster. They're putting money into

better ports and smarter technology to keep the goods flowing.

However, as compared to the top ten countries in the world whose time to import

is higher than UAE is shown in Figure 43.

4.5 Trade of UAE by Religion

In this section, we analyze the trade of UAE with the religious scenarios. We

compared the trade of UAE over the years with Muslim and non-Muslim countries. Figure

44 shows the bar plot of UAE trade with Muslim and non-Muslim countries over the years.

*(All units are in Thousands USD)

Figure 44: Trade of UAE by religion from 2004 to 2020

The code snippet to visualize Figure 44 is used for data manipulation and

visualization using Python, with a focus on analyzing trade data by religion over the years.

• In the first step, we created the mapping dictionaries. Two dictionaries are created:

muslim_map and non_muslim_map.

• muslim_map is a dictionary where each key represents a country (from the

muslim_countries list), and the corresponding value is set to "Muslim".

• non_muslim_map is a dictionary where each key represents a country (from the

non_muslim_countries list), and the corresponding value is set to "Non-

Muslim".

In the next step, all_religion_map is created by combining the

muslim_map and non_muslim_map dictionaries using the ** operator. This results

in a single dictionary where all countries are mapped to their respective religious

categories (either "Muslim" or "Non-Muslim").

• The df_melted DataFrame is updated by adding a new column called

"Religion".

• The "Religion" column is populated by mapping the values in the "Country Name"

column of the DataFrame to the corresponding values in the

all_religion_map. This essentially assigns a religion (either "Muslim" or

"Non-Muslim") to each country in the DataFrame.

For plotting the bar chart, A bar plot is created using the Seaborn library

(sns.barplot).

• The x-axis represents the "Year," the y-axis represents the "Value" (which likely

corresponds to trade values), and the hue (color) of the bars is determined by the

"Religion" column.

• The ci=None argument specifies that no confidence intervals should be plotted.

• Custom colors are defined for the "Muslim" and "Non-Muslim" categories using

the palette parameter.

• Titles and labels are added to the plot.

• The resulting plot is saved as an image file in a specified folder.

The trade distribution of UAE is shown in Figure 45. UAE has large trade values

with both Muslim and Non Muslim countries. The trade of with Non Muslim countries is

very large as compared to the Muslim Countries.

*(All units are in Thousands USD)

Figure 45: Trade distribution by religion.

The trade distribution of Muslim and non-Muslim countries with UAE is visualized

using Python programming language. The first line imports the necessary libraries,

including plt and sns. The second line specifies the figure size to be 12 inches by 7

inches. The third line creates a box plot using the sns.boxplot() function.

The sns.boxplot() function takes the following arguments:

• x: The name of the column that contains the x-axis values.

• y: The name of the column that contains the y-axis values.

• palette: The color palette to use for the plot.

• The fourth line adds a title to the plot using the plt.title() function.

• The fifth line adds an x-axis label to the plot using the plt.xlabel() function.

• The sixth line adds a y-axis label to the plot using the plt.ylabel() function.

• The seventh line ensures that the plot is tightly laid out using

the plt.tight_layout() function.

• The eighth line saves the plot to a file called boxplot_trade_by_religion.png using

the plt.savefig() function.

• The ninth line displays the plot using the plt.show() function.

Results are the ultimate objective of scientific research: here you summarize the

data collected and the statistical treatment of them. The observations and measurements

recorded while conducting the procedures described in the methods section must address

the questions raised in the introduction and any hypotheses formulated there. Overview of

the main findings should be discussed in this section.

4.6 Evaluation of the Effectiveness of Regression Analysis in Predicting GDP and

Evaluation of Trade Trends

In this section, we present a comprehensive exploration of the effectiveness of

regression analysis as a powerful tool for forecasting the intricate relationship between a

nation's Gross Domestic Product (GDP) and its import and export activities, while also

assessing its capacity to predict evolving economic trends. The findings and

methodologies employed shed light on the valuable role that regression analysis plays in

aiding economic projections.

The regression analysis proved to be a robust method for unraveling the intricate

interactions between a country's GDP and its trade dynamics. By scrutinizing historical

data, our model provided a sound understanding of how fluctuations in exports and imports

influenced GDP growth. The multiple regression model, incorporating export and import

variables, offered an effective tool for estimating GDP changes based on trade activities.

The lower Mean Squared Error (MSE) underscored the model's ability to provide accurate

predictions.

An integral facet of our study was the model's ability to predict trends, particularly

in the context of the UAE’s international trade. Utilizing time series data, the model

successfully captured the dynamics of GDP and trade over various years. The inclusion of

lagged variables allowed the model to anticipate future GDP and trade values, making it a

valuable asset for governments and policymakers. The impressive correlation matrix

affirmed that our predictions closely mirrored the actual data, attesting to the model's

ability to discern and extrapolate economic trends.

4.7 Comparison of GDP of the UAE and other High-Income Countries

The United Arab Emirates (UAE) has experienced rapid economic growth in recent

decades, and its GDP is now comparable to that of many other high-income countries.

Figure 46 shows a comparison of the GDP of the UAE and other selected high-income

countries. As the figure shows, the UAE has a comparable GDP than many other high-

income countries, this is due in part to the UAE's reliance on oil and gas exports, which

have generated significant revenue for the country.

It is important to note that the UAE's economy is relatively diversified, and the non-

oil sector now accounts for around 70% of GDP. The UAE government has been investing

heavily in infrastructure and education in recent years, and these investments are paying

off. The UAE's high GDP per capita has led to a significant improvement in living

standards for its citizens. The UAE now has a very high standard of living, with excellent

healthcare, education, and infrastructure.

*(All units are in Billion USD)

Figure 46: GDP of UAE and other high-income countries

Chapter 5: Economic Analysis of Abu Dhabi

Abu Dhabi is the capital of the United Arab Emirates (UAE). It is a major center

for trade, finance, and tourism. In the context of the country, Abu Dhabi plays a significant

role in trade, accounting for over 50% of the country's total non-oil exports.

The economic analysis of Abu Dhabi from 2010 to 2021 shows that the emirate has

experienced significant growth in different parameters like Number of establishments,

Number of employees, Employee Compensation, Total Revenue, Intermediate

consumption, Value Added, Gross fixed capital, and Depreciation. Figure 47 shows the

revenue generated from the year 2010 to 2021 in different sectors. It shows a significant

increase over the year in every sector.

*(All units are in Million AED)

Figure 47: Revenue of Abu Dhabi in different sectors for 2010-2021

*(All units are in Million AED)

Figure 48: Total revenue visualization in Abu Dhabi from 2010-2021

The line graph shown in Figure 48 shows the clear illustration of revenue generated

in Abu Dhabi. It shows how the volume of revenue generated in Abu Dhabi progressed

over the year. There is a decline in 2019 that can be attributed to COVID-19; however, a

significant improvement has been made in generation of revenue after 2020. The top sector

in generating revenue for Abu Dhabi is mining and quarrying, Manufacturing, and

Electricity, gas and air condition supply. The trade analysis of Abu Dhabi in the context

of the UAE shows that the emirate is a major contributor to the country's economy. Abu

Dhabi has a large number of establishments and employees and generates a significant

amount of revenue and value added. This analysis can be used to identify opportunities for

businesses to expand into the Abu Dhabi market or to increase their trade with the emirate.

For example, businesses could target the growing services sector in Abu Dhabi, or they

could invest in new machinery and equipment to increase their production capacity.

The analysis can also be used to inform government policies aimed at promoting

trade and economic growth in Abu Dhabi. A comprehensive confusion matrix is presented

in Figure 49 below that shows the clear picture of revenue generated in each year.

Figure 49: Correlation matrix of Abu Dhabi revenue in different sectors

Chapter 6: Conclusion

6.1 Main Findings of the Study

▪ This analysis of the United Arab Emirates' (UAE) trade relations with major

countries reveals key findings. The United States emerges as the UAE's primary

trade partner, highlighting strong trade ties. China plays a pivotal role as the UAE's

main importer, significantly contributing to the UAE's domestic market. The study

also underscores the value of imports from the USA and significant exports to

China. The UAE maintains a presence in the UK market, and various countries,

including Canada, India, and Australia, contribute to its trade landscape. These

findings shed light on the UAE's dynamic trade relationships and their importance

in its global economic engagement.

▪ The analysis reveals that the UAE has significant trade growth prospects with

various countries, including the UK and Australia, suggesting opportunities for

expanding and diversifying export markets. Furthermore, the consistency in the

UAE's trade relations with its top partners indicates mutual dependability and

economic benefit. Understanding these trade balances is crucial, given the potential

economic repercussions associated with recurring surpluses or deficits. Examining

the data highlights the UAE's dependence on specific nations for imports, which

carries implications for supply chain resilience and diversification. This

information can inform strategies for market expansion and economic growth.

▪ The study's findings from the LSTM model and visualizations indicate a

noteworthy trend in the United Arab Emirates' (UAE) trade. Despite global changes

and various influencing factors, UAE's trade is steadily on the rise. The analysis

reveals a linear upward trend in trade values over the years. While the growth rate

is not exceptionally rapid, it suggests a consistent and positive trajectory. This trend

is indicative of a stable and resilient trade environment, which has proven to be a

key strength of the UAE's economy.

▪ One of the key conclusions drawn from this analysis is that the UAE has

demonstrated its ability to maintain and even grow its trade activities over time,

showing resilience in the face of economic challenges and external factors. This

stability in trade growth highlights the UAE's capacity to adapt and maintain

economic stability, even in the presence of global economic fluctuations, such as

the impact of the COVID-19 pandemic. Additionally, the efforts to diversify the

economy and reduce reliance on oil exports have contributed to the positive trade

patterns observed. These findings underscore the UAE's proactive economic

strategies and its position as a reliable player in international trade.

▪ This analysis offers a visual tool for assessing trade imbalances, revealing

intriguing insights. To underscore the importance of understanding trade dynamics

with key partners, providing valuable insights for policymakers and businesses

seeking to navigate the complexities of global trade, and for those looking to apply

similar methodologies or examine deeper into trade analysis, detailed Python code

snippets for analyzing and visualizing trade analysis are provided.

▪ Geographically, the UAE's prime location at the intersection of Europe, Asia, and

Africa, bolstered by its advanced transportation and logistics infrastructure,

fortifies its status as a preeminent hub for international trade. This analysis explores

diverse scenarios and their impacts on UAE trade dynamics.

▪ The study highlights how COVID-19 has affected the import timeline, extending it

from the pre-pandemic average of 7-10 days to around 10-14 days post-COVID.

The UAE's resilient economic bounce-back has led to increased imports, resulting

in slower processing due to the heightened volume. The global movement of goods

was disrupted, with shipping and flight restrictions causing delays, despite robust

demand.

6.2 Recommendations to Improve Trade Forecasting

As the conclusion of this study, we are giving some recommendations to improve

the accuracy of trade forecasting in the UAE:

6.2.1 Use a Variety of Forecasting Methods

There is no single forecasting method that is always the most accurate. It is

important to use different forecasting models and compare the results of those models.

Using a variety of methods and comparing the results to get a more complete picture is

very important in forecasting.

6.2.2 Use High-Quality Data

Whatever the model is, the most important thing is the data. The accuracy of a

forecast depends on the quality of the data used in the training of any model. Therefore, it

is crucial to use data that is reliable, up-to-date, and relevant to the forecast. The

authenticity of the data must be fulfilled before training a model.

6.2.3 Consider a Variety of Factors

Forecasting a trade analysis, particularly for a country that has a diverse culture,

diverse economy, and a wide range of factors for example for the UAE, many factors can

affect trade, including economic conditions, government policies, and technological

changes. Therefore, it is very important to consider a variety of factors when forecasting

trade. These factors can affect the trade analysis.

6.2.4 Update Forecasts Regularly

As discussed, there could be many factors that can affect the trade analysis of a

country. Forecasts should be updated regularly to reflect changes in the economic and

trade environment. This will help in improving the accuracy of the forecasting model.

6.2.5 Use Expert Judgment

The prediction of forecasting models can be affected due to rapid changes in

technology and continuous changes in government policies. Therefore, expert judgment

can be used to supplement the results of statistical forecasting methods. Experts can

provide a better understanding of factors that may be difficult to quantify, such as the

impact of government policies or technological changes.

6.3 Main Factors that Influence the Trade of UAE

There are various factors we analyzed during the experimentations of this thesis.

These factors influence the trade of UAE. A few factors are elaborated below.

6.3.1 Oil Prices

The UAE is a major exporter of oil and gas. As a result, oil prices have a significant

impact on the UAE's trade balance. When oil prices are high, the UAE's trade balance

improves. When oil prices are low, the UAE's trade balance deteriorates. As we have

observed during COVID-19. The exports of UAE decreased for example for HS 27 during

the pandemic. This affects the overall economy of UAE.

6.3.2 Non-Oil Exports

The UAE has been working to expand its economy and reduce its reliance on oil

exports. As a result, non-oil exports, such as manufactured goods and services, have

become increasingly important to the UAE's economy.

6.3.3 Re-Exports

The UAE is a major re-export hub. The UAE imports goods from other countries

and then re-exports them to other countries. Re-exports are an important source of revenue

for the UAE.

6.3.4 Tourism

Tourism is a important and swiftly growing sector in the United Arab Emirates

(UAE) economy. The country has made significant investments in developing its tourism

infrastructure, including luxurious hotels, entertainment facilities, and world-class

attractions. These investments have yielded substantial returns, attracting tourists from all

corners of the globe. The revenue generated from tourism substantially contributes to the

country's Gross Domestic Product (GDP) and plays a paramount role in the UAE's

economic diversification efforts. Notably, the UAE has been actively working to diversify

its economy away from its historical reliance on oil, and tourism has emerged as one of

the key pillars in achieving this goal. The UAE features a wide range of tourist attractions,

making it a sought-after destination. The UAE offers visitors a blend of both traditional

and modern experiences, ranging from exploring historic souks to indulging in high-end

shopping, theme parks, and world-class entertainment.

The economic impact of tourism in the UAE is considerable. Several sectors benefit

directly from this industry, including the hospitality sector, retail industry, transportation,

and various services. Hotels, restaurants, and shopping malls profit directly from the influx

of tourists, creating numerous employment opportunities and stimulating the local

economy. The hospitality sector stands out as a significant beneficiary as it caters to the

diverse needs and preferences of tourists. Furthermore, the goods and services relevant to

the tourism industry encompass a wide range, including everything from food and

beverages to construction materials used for building hotels and attractions. The UAE

maintains a robust supply chain to support the tourism sector, which involves importing

goods and services, thereby promoting economic activity.

AN EMPIRICAL STUDY ON THE USE OF SECURE PREDICTIVE ANALYTICS FOR IMPROVING TRADE FORECASTING IN THE UAE.

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