How is it possible to perform multivariate classification tasks? #27
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Hi everyone, how is it possible to classify multivariate time-series? For example, when training data is a multidimensional ndarray? Thanks in advance. |
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Answered by
isaksamsten
Nov 26, 2021
Replies: 2 comments 1 reply
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Hi! Many algorithms support multivariate time series out of the box (e.g., |
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Answer selected by
isaksamsten
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Hi!
Thanks for your precious answer, I have a dataset with time-series of equal lengths, and it works well.
I have another question: I am interested in counterfactuals, but I cannot fit algorithm “ShapeletForestCounterfactual” to a multivariate time-series.
I try to fit a ndarray of shape (n_dims, n_timesteps) related to one sample, and the corresponding label, but it doesn’t work: “ValueError: Number of labels=1 does not match number of samples=4”. It seems that the algorithm reads my ndarray as four univariate samples.
If I fit a ndarray of shape (n_samples, n_dims, n_timesteps), I get the following error: “ValueError: Found array with dim 3. Estimator expected <= 2.”
Can you help me?
Thanks again.
Da: Isak Samsten ***@***.***>
Inviato: venerdì 26 novembre 2021 19:23
A: isaksamsten/wildboar ***@***.***>
Cc: LorenzoBonasera ***@***.***>; Author ***@***.***>
Oggetto: Re: [isaksamsten/wildboar] How is it possible to perform multivariate classification tasks? (Discussion #27)
Hi!
Many algorithms support multivariate time series out of the box (e.g., ShapeletForestClassifier). Just fit it with an ndarray of shape (n_samples, n_dims, n_timestep). Unfortunately,wildboar does not support datasets where each dim have different n_timestep. In the example folder there is an example with multivariate time series.
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Hi!
Many algorithms support multivariate time series out of the box (e.g.,
ShapeletForestClassifier
). Just fit it with anndarray
x
of shape(n_samples, n_dims, n_timestep)
andy
of shape(n_samples, )
. Unfortunately, wildboar does not support datasets where each dim have differentn_timestep
. In the example folder there is an example with multivariate time series.