A Note on the Validity of Cross-Validation for Evaluating Time Series Prediction
Christoph Bergmeir (),
Rob Hyndman () and
Bonsoo Koo ()
No 10/15, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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 favor of an out-of-sample (OOS) evaluation. In this paper, we show that the particular setup in which time series forecasting is usually performed using Machine Learning methods renders the use of standard K-fold CV possible. We present theoretical insights supporting our arguments. Furthermore, we present a simulation study where we show empirically that K-fold CV performs favourably compared to both OOS evaluation and other time-series-specific techniques such as non-dependent cross-validation.
Keywords: cross-validation; time series; auto regression. (search for similar items in EconPapers)
JEL-codes: C52 C53 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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