Time Series Cross-Validation

Description

Time series data requires special attention when splitting. In this case, we typically use a method called time series cross-validation, which preserves the temporal order of the data. In this method, the data is split into multiple segments, with each segment representing a fixed time interval. The model is then trained on the past data and tested on the future data. This helps to evaluate the performance of the model in real-world scenarios.

In all cases, it's important to ensure that the split is done randomly but with the same random seed each time to ensure the reproducibility of the results. It's also important to ensure that the split is representative of the underlying data – that is, the distribution of the target variable should be consistent across all sets. Once we have split the data into different subsets for training and testing our model, we can try to find the best set of hyperparameters for our model.