In the last report we made a point on the use of Cross validation with time series. If you want to go in depth with the topic, you can read the following note produced by the University of Melbourne

A Note on the Validity of Cross-Validation for Evaluating Autoregressive Time Series Prediction

One of the most widely used standard procedures for model evaluation in classification and regression is K-fold cross-validation (CV). However, when it comes to time series forecasting, because of the inherent serial correlation and potential non-stationarity of the data, its application is not straightforward and often omitted by practitioners in favour of an out-of-sample (OOS) evaluation. In this paper, we show that in the case of a purely autoregressive model, the use of standard K-fold CV is possible as long as the models considered have uncorrelated errors.

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“A Note on the Validity of Cross-Validation for Evaluating Autoregressive Time Series Prediction”