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
- XE Currency Converter. (n.d.). Retrieved July 08, 2016
- GNP: Trading Economics
- TRADING ECONOMICS | 300.00 INDICATORS | 196 COUNTRIES. (n.d.). Retrieved July 08, 2016,
- Average home sales: National Association of Realtors
- Existing-Home Sales. (n.d.). Retrieved July 08, 2016.
- College tuition: National Center for Education Statistics
- Existing-Home Sales. (n.d.). Retrieved July 08, 2016.
- Weather temperature or precipitation
- Existing-Home Sales. (n.d.). Retrieved July 08, 2016.
- Stock price: Yahoo Finance.
- Yahoo Finance - Business Finance, Stock Market, Quotes, News. (n.d.). Retrieved July 08, 2016,
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 ânext periodâ prediction for each method.