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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?
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?
Explanation:
In time series forecasting problems, the temporal ordering of the data is an important factor to consider. Traditional cross-validation techniques, such as K-fold or stratified cross-validation, do not take this ordering into account and can lead to biased estimates of the model's performance. Time series cross-validation, on the other hand, is specifically designed for time series data and ensures that the temporal structure of the data is preserved during the cross-validation process. This makes it the most appropriate choice for evaluating the performance of time series forecasting models.