Time Series Cross-Validation Calculator

Expanding window: Training set grows with each split
No data leakage: Future data never seen during training
Realistic evaluation: Mimics real forecasting scenarios

Validate forecasting models respecting temporal order

Time Series Data (comma-separated, chronological order):

Number of Splits:

Min Training Size:

Time series CV respects the temporal order of data, always training on past observations and testing on future ones.

Key characteristics:

Note: Regular k-fold CV is inappropriate for time series due to temporal dependencies.