# Using Low Frequency Labels with High Frequency Features

I am trying to build a model (most likely a regression or random forest regression) for quarterly financial data. My training data has a daily cadence, but I am not sure how to work with these to predict a quarterly dependent variable. Clearly I could summarize these by quarter (avg, mean, etc.) but that would result in few total observations, and it feels like that is throwing out some valuable variance to train on.

Thanks for the help!

These windows can be overlapping, eg by a factor $$o$$%, no need to be non-overlapping.