The correct answer is: Relative Expression Split mode.

The Relative Expression Split mode lets you define a condition based on row indices or timestamp values so you can create a contiguous training period and a contiguous testing period which preserves temporal ordering for time series forecasting. Using an expression you can keep the earliest portion of the dataset for training and the most recent portion for testing which prevents lookahead bias and better reflects real forecasting scenarios.

Regular Expression Split is incorrect because regular expressions are used to match and split text based on patterns and they do not provide a natural way to select contiguous rows by time or index to preserve chronological order.

Split Rows with Randomized option enabled is incorrect because enabling randomization shuffles rows and destroys temporal order which can leak future information into the training set and invalidate time series evaluation.

Recommender Split is incorrect because it is tailored for recommender system data partitioning and user item scenarios and it does not provide the straightforward contiguous time based cut needed for general time series forecasting.

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