Problem: This assignment addresses forecasting your selected Y data (dependent variable) using an exponential smoothing technique. Note: Do not use the X (independent) variables in this exercise. Use only one exponential smoothing method -- the best that applies. Do not use any other forecasting techniques in this assignment.

1. Tell me why you selected the appropriate exponential smoothing method by commenting on your Y data characteristics. (you should use a time series plot and autocorrelations to do this),
2. Apply the appropriate exponential smoothing forecast technique to your Y variable excluding the last year of data (hold out period). Show the Y data, fitted values and residuals in excel format and show your exponential smoothing model coefficients. (Find the correct coefficient and not just use the default values.)
3. Evaluate the "Goodness To Fit" using at least two error measures -- RMSE and MAPE.
4. Check the "Fit" period residual mean proximity to zero and randomness with a time series plot; check the residual time series plot and autocorrelations (ACFs) for trend, cycle and seasonality.
5. Evaluate the residuals for the "Fit" period by indicating the residual distribution using a histogram (normal or not and random or not),
6. Comment on the acceptability of the model's ability to pick up the systematic variation in your Fit period actual data.
7. Develop a one year forecast (for the hold out period).
8. Evaluate the "Accuracy" of the forecast for the "hold out period" using RMSE and MAPE error measures used from forecast period residuals and comment them.
9. Do the forecast period residuals seem to be random relative to the hold out period data? Check the forecast period time series plot of the residuals.
10. Did the error measures get worse, remain the same or get better from the fit to the hold out period? Do you think the forecast accuracy is acceptable?

Price: $28.09

Solution: The downloadable solution consists of 16 pages, 1209 words and 12 charts.
Deliverable: Word Document