![]() When choosing models, it is common practice to separate the available data into two portions, training and test data, where the training data is used to estimate any parameters of a forecasting method and the test data is used to evaluate its accuracy. The accuracy of forecasts can only be determined by considering how well a model performs on new data that were not used when fitting the model. Consequently, the size of the residuals is not a reliable indication of how large true forecast errors are likely to be. It is important to evaluate forecast accuracy using genuine forecasts. 12.9 Dealing with missing values and outliers.12.8 Forecasting on training and test sets. ![]() 12.7 Very long and very short time series.12.5 Prediction intervals for aggregates.12.3 Ensuring forecasts stay within limits.10.7 The optimal reconciliation approach.10 Forecasting hierarchical or grouped time series. ![]()
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