There are a few reduction strategies from outlier or anomaly detection to probabilistic regression.
We could support these in skpro, with a pyod compatible interface. This would in turn allow to use the resulting pyod compatible estimators in sktime anomaly and changepoint detectors.
Reducers we could implement:
quantile based: outlier is extremity of predictive quantile
density based: outlier is log-pdf of observed value
loss based: outlier is predictive loss
the above two can be seen as special cases, for log-loss or constraint violation
The above being applied to conditional or unconditional distribution estimates.
There are a few reduction strategies from outlier or anomaly detection to probabilistic regression.
We could support these in
skpro, with apyodcompatible interface. This would in turn allow to use the resultingpyodcompatible estimators insktimeanomaly and changepoint detectors.Reducers we could implement:
The above being applied to conditional or unconditional distribution estimates.