What are some common pitfalls when using cross-validation in time series forecasting?

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What are some common pitfalls when using cross-validation in time series forecasting?

Asked on Dec 22, 2025

Answer

When using cross-validation for time series forecasting, it's crucial to account for the temporal dependencies inherent in the data. Unlike traditional cross-validation, time series data requires methods that respect the chronological order, such as time series split or walk-forward validation, to avoid data leakage and ensure realistic model evaluation.

Example Concept: In time series forecasting, a common pitfall is using standard k-fold cross-validation, which can lead to data leakage by mixing past and future data. Instead, techniques like time series split or walk-forward validation should be employed. These methods maintain the temporal order by training on past data and validating on future data, thus preserving the sequence and providing more reliable performance estimates.

Additional Comment:

Ensure that the validation set always follows the training set in time.
Consider using rolling-origin or expanding window approaches to simulate real-world forecasting scenarios.
Be cautious of seasonality and trends, which can affect model performance if not properly accounted for in the validation strategy.

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