Description
Unit 8 DiscussionDiscussion Topic
Time Series Model
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/) XE Currency Converter. (n.d.). Retrieved July 08, 2016, from http://www.xe.com/currencyconverter
- GNP: Trading Economics (http://www.tradingeconomics.com/united-states/gross-national-product) TRADING ECONOMICS | 300.00 INDICATORS | 196 COUNTRIES. (n.d.). Retrieved July 08, 2016, from http://www.tradingeconomics.com/united-states/gros...
- Average home sales: National Association of Realtors (http://www.realtor.org/topics/existing-home-sales) Existing-Home Sales. (n.d.). Retrieved July 08, 2016, from http://www.realtor.org/topics/existing-home-sales
- College tuition: National Center for Education Statistics (https://nces.ed.gov/fastfacts/display.asp?id=76) Existing-Home Sales. (n.d.). Retrieved July 08, 2016, from http://www.realtor.org/topics/existing-home-sales
- Weather temperature or precipitation: (http://www.weather.gov/help-past-weather) Existing-Home Sales. (n.d.). Retrieved July 08, 2016, from http://www.realtor.org/topics/existing-home-sales
- Stock price: Yahoo Finance (https://finance.yahoo.com) Yahoo Finance - Business Finance, Stock Market, Quotes, News. (n.d.). Retrieved July 08, 2016, from https://finance.yahoo.com/
Once you have historical data, address the following:
- State the variable you are forecasting.
- Collect data for any time horizon (daily, monthly, yearly). Select at least eight data values.
- Use Excel QM to forecast using moving average, weighted moving average and exponential smoothing (see video in Live Binder).
- 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. Be sure to include the MAD (mean absolute deviation) for each method.
- Clearly state the “next period” prediction for each method.
First response: Choose a classmate’s post. Use the same data, forecast a trend projection using Excel QM. Share the graph and “next period” prediction. Based on the graph, do you think this is a good model for this variable?
Second response: Choose another classmate’s post. Compare the MAD (mean absolute deviation) of all three forecasts (moving average, weighted moving average and exponential smoothing) and state which forecasting method gives the most accurate forecast.
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Explanation & Answer
See attached. I have already posted onto the campus site.
Discussion post 8 replies
Hello Philip,
Good job! Based on your data on college tuition, I created a trend projection using Excel QM.
The following output was obtained.
Data
Period
Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Year 10
Interce
pt
Slope
Forecas
t
Forecasts and Error Analysis
Demand
(y)
18,072
18,268
18,973
19,404
19,912
20,315
20,839
21,291
21,875
22,432
Period(
x)
1
2
3
4
5
6
7
8
9
10
17445.2
489.6181
82
22831
Forecast
17934.82
18424.44
18914.05
19403.67
19893.29
20382.91
20872.53
21362.15
21851.76
22341.38
Error
Absolute
Squared
Abs Pct
Err
137.1818
-156.436
58.94545
0.327273
18.70909
-67.9091
-33.5273
-71.1455
23.23636
90.61818
137.1818
156.4364
58.94545
0.327273
18.70909
67.90909
33.52727
71.14545
23.23636
90.61818
18818.85
24472.34
3474.567
0.107107
350.0301
4611.645
1124.078
5061.676
539.9286
8211.655
00.76%
00.86%
00.31%
00.00%
00.09%
00.33%
00.16%
00.33%
00.11%
00.40%
Total
2.91E-11
658.0364
66664.87
03.36%
Average
2.91E-12
65.80364
6666.487
00.34%
Bias
MAD
SE
MSE
MAPE
91.28586
11
Correlation
Coefficient of
determination
0.998319
0.996641
Regression
30,000
20,000
10,000
0
0
2
4
Series1
6
8
10
12
Linear (Series1)
Based on the output, the predicted college tuition for 2016 is $22831.
Based on the graph, there appears to be a linear trend on the college tuition based on years. The
model is a good fit since the trend line appears to be linear and all the points appear to be on the
trend line. The coefficient of determination is al...