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.



David Burns

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