# Why can't I do a traditional train/test split for timeseries forecasting?

Colleagues of mine were tasked with building a forecasting model. They intended on doing a train/test split for cross validation. I know that when training models for forecasting, a "walk forward" validation is more often used. Why is that?

• Because usually your model for the data $Y_t$ at time $t$ will depend on its past values $Y_{t-1}, Y_{t-2}, \dots, Y_{t-p}$. So when you validate your prediction $\widehat{Y}_t$, you need $Y_{t-1}, Y_{t-2}, \dots, Y_{t-p}$ to do so. In contrast, in the cross-sectional setting, $Y_t$ will not depend on its own past and there is also no natural order (like the time ordering of a time series) in the data. – Jeremias K Dec 9 '17 at 19:07