Discussion unit 8 Business

Unit 8 Discussion Example - Initial Post A time series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. Here are some examples of variables you can use to forecast. You may use a different source other than the ones listed (be sure to reference the website). There are many other variables you can use, as long as you have values that are recorded at successive intervals of time.

 Currency price: XE (http://www.xe.com/currencyconverter/ )

 GNP: Trading Economics (http://www.tradingeconomics.com/united-states/gross-national-product )

 Average home sales: National Association of Realtors (http://www.realtor.org/topics/existing-home- sales )

 College tuition: National Center for Education Statistics (https://nces.ed.gov/fastfacts/display.asp?id=76 )

 Weather temperature or precipitation: (http://www.weather.gov/help-past-weather )

 Stock price: Yahoo Finance (https://finance.yahoo.com ) Once you have historical data, address the following:

1. State the variable you are forecasting. 2. Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data values. 3. Use Excel QM to forecast using moving average, weighted moving average and exponential smoothing

(see video in Live Binder). 4. Copy/paste the results of each method. Be sure to state the number of periods used in the moving

average method, the weights used in the weighted moving average, and the value of alpha used in exponential smoothing.

5. Clearly state the “next period” prediction for each method.

****************************************************************************************** I will use the National Association of Realtors Website (http://www.realtor.org/topics/existing-home-sales )

and I downloaded the “Single-Family Existing Home Sales and Prices” spreadsheet for Database work.

1. I will look at the (not-seasonally adjusted) median sale price for the West column over the past year by

month (May 2015 – May 2016). Here is the data:

Year West

2015 May 325,800

2015 Jun 331,300

2015 Jul 329,300

2015 Aug 322,000

2015 Sep 322,200

2015 Oct 324,200

2015 Nov 321,700

2015 Dec 324,900

2016 Jan 313,400

2016 Feb 312,300

2016 Mar 322,500

2016 Apr 337,800

2016 May 348,100

2&3)

3-Month Moving Average – forecast is $336,133

3-Month Weighted Moving Average – forecast is $340,400

Weights are 3 = most recent month, 2 = 1-month prior, 1 = 2-months prior

Forecasting Moving averages - 3 period moving average

Num pds 3

D a ta Fore ca sts a nd E rror Ana lysis

Period Demand Fore ca st E rror Absolute S qua re d Abs P ct E rr

Month 1 $325,800

Month 2 $331,300 331300 331300 1.1E+11 100.00%

Month 3 $329,300 329300 329300 1.08E+11 100.00%

Month 4 $322,000 $328,800 -6800 6800 46240000 02.11%

Month 5 $322,200 $327,533 -5333.33 5333.333 28444444 01.66%

Month 6 $324,200 $324,500 -300 300 90000 00.09%

Month 7 $321,700 $322,800 -1100 1100 1210000 00.34%

Month 8 $324,900 $322,700 2200 2200 4840000 00.68%

Month 9 $313,400 $323,600 -10200 10200 1.04E+08 03.25%

Month 10 $312,300 $320,000 -7700 7700 59290000 02.47%

Month 11 $322,500 $316,867 5633.333 5633.333 31734444 01.75%

Month 12 $337,800 $316,067 21733.33 21733.33 4.72E+08 06.43%

Month 13 $348,100 $324,200 23900 23900 5.71E+08 06.87%

Total 682633.3 745500 2.2E+11 225.65%

Ave ra ge 56886.1 62125 1.8E +10 18.80%

Bia s M AD M S E M AP E

S E 148161

N e xt pe riod $336,133

Forecasting Weighted moving averages - 3 period moving average

D a ta Fore ca sts a nd E rror Ana lysis

Period Demand Weights Fore ca st E rror Absolute S qua re d Abs P ct E rr

Month 1 $325,800 1

Month 2 $331,300 2

Month 3 $329,300 3

Month 4 $322,000 329383.3 -7383.33 7383.333 54513611 02.29%

Month 5 $322,200 325983.3 -3783.33 3783.333 14313611 01.17%

Month 6 $324,200 323316.7 883.3333 883.3333 780277.8 00.27%

Month 7 $321,700 323166.7 -1466.67 1466.667 2151111 00.46%

Month 8 $324,900 322616.7 2283.333 2283.333 5213611 00.70%

Month 9 $313,400 323716.7 -10316.7 10316.67 1.06E+08 03.29%

Month 10 $312,300 318616.7 -6316.67 6316.667 39900278 02.02%

Month 11 $322,500 314766.7 7733.333 7733.333 59804444 02.40%

Month 12 $337,800 317583.3 20216.67 20216.67 4.09E+08 05.98%

Month 13 $348,100 328450 19650 19650 3.86E+08 05.64%

Total 21500 80033.33 1.08E+09 24.24%

Ave ra ge 2150 8003.33 1.1E +08 02.42%

Bia s M AD M S E M AP E

S E 11607.9

N e xt pe riod $340,400

Exponential Smoothing, alpha = 0.25 – forecast is $330,413

Forecasting Exponential smoothing

Alpha 0.25

D a ta Fore ca sts a nd E rror Ana lysis

Period Demand Fore ca st E rror Absolute S qua re d Abs P ct E rr

Period 1 $325,800 325800 0 0 0 00.00%

Period 2 $331,300 325800 5500 5500 30250000 01.66%

Period 3 $329,300 327175 2125 2125 4515625 00.65%

Period 4 $322,000 327706.3 -5706.25 5706.25 32561289 01.77%

Period 5 $322,200 326279.7 -4079.69 4079.688 16643850 01.27%

Period 6 $324,200 325259.8 -1059.77 1059.766 1123103 00.33%

Period 7 $321,700 324994.8 -3294.82 3294.824 10855867 01.02%

Period 8 $324,900 324171.1 728.8818 728.8818 531268.7 00.22%

Period 9 $313,400 324353.3 -10953.3 10953.34 1.2E+08 03.50%

Period 10 $312,300 321615 -9315 9315.004 86769299 02.98%

Period 11 $322,500 319286.3 3213.747 3213.747 10328170 01.00%

Period 12 $337,800 320089.7 17710.31 17710.31 3.14E+08 05.24%

Period 13 $348,100 324517.3 23582.73 23582.73 5.56E+08 0.067747006

Total 18451.8 87269.54 1.18E+09 26.41%

Ave ra ge 1419.37 6713.04 9.1E +07 02.03%

Bia s M AD M S E M AP E

S E 10372

N e xt pe riod $330,413