What validation methods for time series models exclude looking into the future during training?

When working with time series, it is important to avoid information leakage from the future into the model training to ensure a fair assessment of its quality. Special validation methods are used:

• Time Series Split (Walk-Forward Validation) — data is split into consecutive time windows. The model is trained on data up to a certain point and tested on the next time interval. Then the window shifts forward.

• Rolling Forecast Origin — similar to Time Series Split, but with gradual expansion of the training set, mimicking a real forecasting scenario.

• Blocked Cross-Validation — data is divided into blocks by time, with training and testing blocks not overlapping or neighboring to exclude correlation.

• Purged K-Fold — when splitting into folds, periods close to the boundaries between training and testing sets are removed to prevent leakage.

These methods ensure that during training, the model does not see future data, which is critical for time series.