Rolling window cross-validation is a technique used to assess the predictive performance of a model on time series data. It involves training the model on a specific time period and then testing it on a subsequent period, progressively rolling the training window forward in time. This method is particularly useful for evaluating how well a model can predict future values based on past data, making it essential for regression tasks with time-dependent structures.
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