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Inverse transform for dimensionality reduction algos #103
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Hi, Sorry for the delayed response. In the
Finally pyts.approximation.SymbolicFourierApproximation is the pipeline of pyts.approximation.DiscreteFourierTransform followed by pyts.approximation.MultipleCoefficientBinning. It's also important to note most of these algorithms learn the approximation of each time series independently, and therefore these algorithms do not learn to reduce the original feature space (like PCA or KernelPCA that learn a projection of the original feature space onto a subspace). Back to your question, it means that:
I will try work on this in the near future! Let me know what you think. Best, |
Description
Hello! Is there a way to inverse_transform any of the 'approximation' methods? I have a dataset of simple 20,0001D timeseries only 30 units long (i.e., [20,000 x 30]) and I want to reduce this to say 5 (i.e. to [20,000 x 5]). So far I have been using PCA and kernel PCA, neither of which are meant for time series work, and I would like to compare performance with the various algorithms in this package, but unfortuantely I did not find a way to inverse-transform the reduced time series.
Steps/Code to Reproduce
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