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I am new to data science modelling so apologies if using wrong terminology in advance.

I have a standard time series dataset of historical prices which is used to train/test a simple Random Forest classifier model which predicts the returns direction (+/-).

I also have a few general statistics for 'day of the week direction' eg. frequency counts: Monday UP=120, Monday DOWN=90, Tuesday UP=67, Tuesday DOWN=50, Friday UP=55, Friday DOWN=181.

How can I combine the results from the time series RF model with the bias from the statistics into a new model? eg. give more weight to positive/up predictions on Monday, and more weight to negative/down predictions on Friday?

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There are multiple approaches. Two of them are:

  • Convert time series data to structured data
  • Convert your statistic data to time series data

This article is a good introduction.

For your second question about giving more weight to some features, please read this post.

Hope it helps.

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