
Tsfresh – Automatic extraction of relevant features from time series - restapi
https://github.com/blue-yonder/tsfresh
======
placebo
I've implemented a peaks/troughs feature extraction in Javascript a few years
ago as a basis for some larger analysis project. It works at O(n) of the
number of data points. You can play with a demo here:

[http://nocurve.com/2014/01/12/finding-peaks-and-troughs-
in-a...](http://nocurve.com/2014/01/12/finding-peaks-and-troughs-in-a-noisy-
curve/)

------
madengr
Would be nice in an oscilloscope, though many have a good set of waveform
measurements.

------
ash9r
sure but would they be relevant, good features?

~~~
_lm_
> To avoid extracting irrelevant features, the TSFRESH package has a built-in
> filtering procedure. This filtering procedure evaluates the explaining power
> and importance of each characteristic for the regression or classification
> tasks at hand.

> It is based on the well developed theory of hypothesis testing and uses a
> multiple test procedure. As a result the filtering process mathematically
> controls the percentage of irrelevant extracted features.

Here's the paper on this:
[https://arxiv.org/abs/1610.07717](https://arxiv.org/abs/1610.07717)

It seems that the relevance of the features is somewhat tunable based on the
p-value you choose for the statistical tests. (Every feature selection
algorithm I can think of has some tunable parameter, although the information
theoretic ones just depend on the length of features you're willing to
consider.)

~~~
MaxBenChrist
The individual feature significance tests do not have any parameter, they just
generate the p-values.

The only parameter that one can tune is the overall percentage of irrelevant
extracted features. That is the expected FDR of the Benjamini yakutieli
procedure.

------
zump
Why not use an RNN?

~~~
MaxBenChrist
what for? for the feature extraction part?

