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Theoretically this should be compatible with the encoder - decoder style framework we have here?
The text was updated successfully, but these errors were encountered:
Alternatively, adding a method that returns the extracted features after a forecasting model is trained.
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Thanks for the suggestion, Unfortunately, we are busy with some other work these days and may add this feature in the near future.
However, here are some hints: you could write a new pipeline for time series classification, which should be something similar to the tabular classification pipline and reimplement the [classification network] (https://github.com/automl/Auto-PyTorch/blob/master/autoPyTorch/pipeline/components/setup/network/base_network.py), adding them to the pipeline. Additionally, the network backbone might also need to be modified accordingly: Auto-regressive needs to be removed.
Then you need to create a new class for time series classification task that is inherent from this class. Then everything should work as expected
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Theoretically this should be compatible with the encoder - decoder style framework we have here?
The text was updated successfully, but these errors were encountered: