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 …