In time series cross validation, the dataset is split into multiple training and validation sets while preserving the temporal order. One common approach is the rolling-origin method, where the model is trained on an initial time period and validated on the subsequent period. The training window is then expanded or moved forward, and the process is repeated. Another approach is the sliding window method, where both the training and validation windows move forward in time but remain fixed in size.
