Nell’ultimo Report abbiamo fatto un punto sull”utilizzo di Cross validation per serie storiche. Se vuoi approfondire l’argomento, puoi leggere la seguente nota prodotta dall’Università di 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”