Abstract
This article presents a unifying approach of several procedures in time series. First, we show that quadratic discrimination provides a framework for deriving model selection criteria for time series. Second, we establish a connection between model selection criteria and goodness of fit tests. Finally, we show that the outlier detection problem in ARIMA models can be seen as a particular case of model selection. Therefore, the problems of model selection, discrimination, goodness of fit tests and outliers in time series can be treated under the same principles.
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