Splits training and test data chronologically by similarities based on a K-means cluster. Can be helpful to split data chronologically keeping relevant portions in both train and test datasets. Actions: Trains cluster from input dataset. Processes batch centroid over the same dataset. For each cluster label, creates specific dataset and splits train-test chronologically with given proportions. Merges all train and test portions separately providing a final train and test database. Requirements: The original dataset needs to be ordered chronologically.
Splits training and test data chronologically by similarities based on a K-means cluster. Can be helpful to split data chronologically keeping relevant portions in both train and test datasets. Actions: Trains cluster from input dataset. Processes batch centroid over the same dataset. For each cluster label, creates specific dataset and splits train-test chronologically with given proportions. Merges all train and test portions separately providing a final train and test database. Requirements: The original dataset needs to be ordered chronologically.
Splits training and test data chronologically by similarities based on a K-means cluster. Can be helpful to split data chronologically keeping relevant portions in both train and test datasets. Actions: Trains cluster from input dataset. Processes batch centroid over the same dataset. For each cluster label, creates specific dataset and splits train-test chronologically with given proportions. Merges all train and test portions separately providing a final train and test database. Requirements: The original dataset needs to be ordered chronologically.
