What are reasonable criteria for selecting one time-series method over another?

What will be an ideal response?

Forecast error measures provide important information for choosing the best forecasting method for a service or product. They also guide managers in selecting the best values for the parameters needed for the method: n for the moving average method, the weights for the weighted moving average method, alpha for the exponential smoothing method, and when regression data begins for the trend projection with regression method. The criteria to use in making forecast method and parameter choices include 1. minimizing bias (CFE); 2. minimizing MAPE, MAD, or MSE; 3 . maximizing r2; 4. meeting managerial expectations of changes in the components of demand; and 5. minimizing the forecast errors in recent periods. The first three criteria relate to statistical measures based on historical performance, the fourth reflects expectations of the future that may not be rooted in the past, and the fifth is a way to use whatever method seems to be working best at the time a forecast must be made.