You are working on a time series forecasting problem and have decided to use cross-validation to evaluate your model's performance. Which type of cross-validation is most appropriate for time series data, and why?

Simulated

K-fold cross-validation, because it is a widely used technique for evaluating the performance of machine learning models.

20.0%

Stratified cross-validation, because it ensures that each fold has a balanced representation of the target variable.

4.0%

Time series cross-validation, because it takes into account the temporal ordering of the data.

72.0%

Leave-one-out cross-validation, because it provides a more robust estimate of the model's performance by using a large number of folds.

4.0%

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