Cross-validation is used for tuning the hyperparameters and producing measurements of model performance. With the time series data, we can’t use the traditional cross-validation technique due to two main reasons which are as follows -
- Temporal dependencies
- Arbitrary Choice of Test Set
For time-series data, we use nested cross-validation that provides an almost unbiased estimate of the true error. A nested CV consists of an inner loop for parameter tuning and an outer loop for error estimation.