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Why is standard k-fold cross-validation inappropriate for time series data?

A Random splits let the model train on future data and predict the past
B Time series data cannot be split
C It requires too much memory
D Folds must be equal in size

Answer

Random splits let the model train on future data and predict the past

Forward-chaining validation trains on everything before a cutoff and validates after it, which reflects how the model will actually be used.

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