Unlike traditional cross-validation, where data can be randomly split into training and test sets, time series cross validation must respect the temporal order of data points. A common method is the rolling window approach, where the model is trained on a fixed window of data and tested on the subsequent time points. This process is repeated by shifting the window forward in time. Another method is expanding window cross validation, where the training set grows with each iteration, always including all previous observations.
