Using a Microsoft Word Document 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 variable There are many variables you can use, as long as you have values that are recorded at successive intervals of time Here are some examples of variables you can use to forecast 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) Select a dataset with a variable you would like to forecast You may use a different source other than the ones listed above (be sure to reference the website) State the variable you are forecasting Select at least eight consecutive data values Using the Time Series Forecasting Templates, determine the following for the selected variable moving average, weighted moving average, and exponential smoothing 5 Copy paste the results of each method into your post Be sure to state the number of periods used in the moving average method the weights used in the weighted moving average the value of used in exponential smoothing 6 Clearly indicate the next period prediction for each method 7 Choose one of the following Write a sentence that identifies the prediction Circle, draw, etc on the chart to indicate which value is the prediction for the next time period 8 Suppose that the forecasting results are from three different branches of a company Based on the MAD (mean absolute deviation) value, how would you prioritize the need to update the forecasting methods to improve overall predictions Note The higher the MAD value the worse the forecast

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Using a Microsoft Word Document: A time series model is a forecasting technique that attempts to predict the future values of a variable by using

"Using a Microsoft Word Document:

"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 variable. There are many variables you can use, as long as you have values that are recorded at successive intervals of time. Here are some examples of variables you can use to forecast. "

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)

Select a dataset with a variable you would like to forecast. You may use a different source other than the ones listed above (be sure to reference the website).
State the variable you are forecasting.
Select at least eight consecutive data values.
Using the Time Series Forecasting Templates, determine the following for the selected variable:

moving average,
weighted moving average, and
exponential smoothing

5. Copy/paste the results of each method into your post. Be sure to state:

the number of periods used in the moving average method.
the weights used in the weighted moving average.
the value of used in exponential smoothing.

6. Clearly indicate the "next period" prediction for each method.

7. Choose one of the following:

Write a sentence that identifies the prediction.
Circle, draw, etc. on the chart to indicate which value is the prediction for the next time period.

8. Suppose that the forecasting results are from three different branches of a company.

- Based on the MAD (mean absolute deviation) value, how would you prioritize the need to update the forecasting methods to improve overall predictions? Note: The higher the MAD value the worse the forecast"