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Forecasts ought to serve a specific purpose. They should not be made “just because” but be useful in the making of a decision. The decision then dictates the kind of forecast that should be made – its form and its time horizon(s). It also dictates how the forecast should be evaluated – a forecast only being as good as the quality of the decisions it enables.
Example 2.1 Retailers typically need to order some amount of milk that they will sell over the next week. They do not know how much they will sell so they usually order, hoping to satisfy, let us say, 95% of demand. This situation tells us that the forecasts need to be made a week ahead, they should be cumulative (considering the overal demand during a week before the next order) and that they should focus on an upper bound of a 95% prediction interval. Producing only point forecasts would not be useful in this situation.
When you understand how your system works and what sort of forecasts you should produce you can start an evaluation process; measuring the performance of different forecasting models / methods and selecting the most appropriate for your data. There are different ways to measure and compare the performance of models / methods In this chapter, we discuss the most common approaches.