Time series cross-validation

Time series cross-validation is a method used to evaluate how well a predictive model performs on data collected over time. Unlike random sampling, it respects the chronological order, so earlier data is used to train the model and later data to test its accuracy. This process involves dividing the data into consecutive segments, training on one segment, and testing on the next, then shifting forward for multiple rounds. It helps ensure the model can make reliable forecasts in real-world situations where future data depends on past trends, providing a realistic assessment of its predictive power over time.