Time series cross validation tool, supplementing or replacing relevant existing sklearn libraries.
The package provides quick train/test split indexing for cross validation, specifically optimized for time series data. There are two primary output options:
- Expanding Window - in which the training window becomes larger with each fold and is always overlapping with part of the previous.
- Rolling Window - in which the training window is of a fixed pre specified dimension, and it may or may not overlap depending on the rolling step specified.
''' from time_cross_validation import TimeCV import pandas as pd
#sample X and Y variables: X = pd.DataFrame([10,20,10,4,5,1,7,20]) Y = pd.DataFrame([5,1,7,20,10,20,10,4])
CV = TimeCV(X, train_sample_size = 3, test_sample_size = 3, step = 1) for train_index, test_index in CV.expanding_train_test_split(): x_train = X.iloc[train_index] x_test = X.iloc[test_index] '''
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