how to implement walk forward testing in sklearn?

Question

In sklearn, GridSearchCV can take a pipeline as a parameter to find the best estimator through cross validation. However, the usual cross validation is like this: to cross validate a time series data, the training and testing data are often splitted like this: That is to say, the testing data should be always ahead of training data. My thought is: Write my own version class of k-fold and passing it to GridSearchCV so I can enjoy the convenience of pipeline. The problem is that it seems difficult to let GridSearchCV to use an specified indices of training and testing data. Write a new class GridSearchWalkForwardTest which is similar to GridSearchCV, I am studying the source code grid_search.py and find it is a little complicated. Any suggestion is welcome. JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

I think you could use a Time Series Split either instead of your own implementation or as a basis for implementing a CV method which is exactly as you describe it. After digging around a bit, it seems like someone added a max_train_size to the TimeSeriesSplit in this PR which seems like it does what you want.