Random splitting temporal data makes the target interpolation not forecasting

Random splitting destroys the direction of temporal data completely. When splitting randomly into train/test, we often have information “around” a certain row in both splits, which allows us to “fill the gap” by predicting what’s missing. We effectively perform local smoothing. The model memorizes local patterns instead of extrapolating forward.