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Chapter 3 Forecasting process and forecasts evaluation

One more thing to discuss before moving to the meat of the textbook, is how to evaluate forecasting models and methods. We should start by saying that forecasting needs to be done for a specific purpose. It should not be done just because you can do that. Forecasts should be useful for specific decisions. And these decisions dictate what forecasts, in what form and on what horizons are needed.

Example 3.1 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 done for 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 just point forecasts might not be useful in this situation.

When you understand how your system works and what sort of forecasts you should produce, then you can start an evaluation process, measuring the performance of several forecasting models / methods and selecting the most appropriate for your data. There are different ways how the performance of models / methods can be measured and compared. In this chapte, we discuss the most common approaches.