Below is a pair plot of the types of distributions (Time Series) I've been attempting to run models upon. Two of the features are strongly collinear (the distributions of last 2 on the diagonal of the plot) and are heavily positively skewed shown below along with another fairly heavily negatively skewed distribution (the 2nd on the diagonal).
Enclosed is the data I'm working with:
I've run a few attempts of multiple linear regression, ridge, random forest, and have tried log transforming the data to explore the behaviour better.
The coefficients of determination are far from what we'd like (~0.31) running on Python's Statsmodel OLS + sklearn regressors. I've also tried modeling the features separately to the target feature with just as bad (if not worse) outcomes.
When dealing with this type of situation is the best way to deal with it to use non log transformations to some of the features and not the others? I've also thought about possibly breaking up the models into 3 thresholds to break the skewedness.
I've also thought about looking more into Gumbel distributions or Intensity Duration Frequency modeling as those seem to deal directly with these types of scenarios and require a great deal of research on my part (willing to do if it's a step in the right direction).
Any suggestions on how to proceed with these distributions?