I implemented a meta-estimator and transformers for time series /
sequence learning with sliding window segmentation. It can be used for
classification, regression, or forecasting - supporting multivariate
time series / sequences and contextual (time-independent) data. It can
learn time series or contextual targets.
It is (mostly) compatible with the sklearn model evaluation and
selection tools - despite changing the number of samples and the target
vector mid pipeline (during segmentation).
I've created a pull request on related_projects.rst - but thought I
would share it here for those of you interested in this area.
https://github.com/dmbee/seglearn
Cheers,
David Burns
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