I want to predict sold number of item in every day in month. But data is too huge, so I train it with incrementall learning ex: sklearn.neural_network.MLPRegressor
I train data per 3 months ex: 1st training with data containing from Jan. to Mar. Then train from Apr. to Jun. Then I evaluate with Feb. data, and I found mse will grow up when train incrementally till Oct. to Dec. It seems catastrophic forgetting happens that prediction of Feb. is very mess but prediction of near month ex: Dec. is good because the training of Dec. is near. Tune parameters doesn't improve well because catastrophic forgetting still happen frequently. How should I improve that? Change another way or training period? Or I should split model into 12 model and one model is reponsible for a month?
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