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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

Abstract: 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
Date: 2015
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