I am trying to build a, regressive, predictive model for a target time-series that is heavily skewed.
You could think of the target as being like earthquake magnitudes or heavy rainfall. Most of the time we sit in the relatively boring head of the distribution, but we want to predict the interesting 'extreme' events.
The distribution of target values looks like this (in a histogram with Bayesian block sizing)
Are the approached legitimate alone or in combination?:
- Predict in
f(x)is used to produce a zero-mean, unit-variance distribution.
- Prefer non-linear (e.g. tree based, Support-Vector-Regressors with non-linear kernels) estimators.
- In choosing samples for learning, validation and testing: relatively oversample the right-tail of the target distribution.
Is there something else I should try?
In case it helps with context I'm using python and sklearn.