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Which data-splitting method is appropriate when evaluating a future-prediction model on time-series stock price data?
①Randomly shuffle the data and split it into training and validation sets.
②Maintain chronological order and split the past for training and the future for validation.
③Mix all the data and perform cross-validation.
④Randomly select data from each day for validation.
⑤Evaluate using only the training data without using any test data.