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Tsfresh – Automatic extraction of relevant features from time series
- ash9r 10y agosure but would they be relevant, good features?
- BWStearns 10y agoGiven the first pass seems often to be picking some standardish stuff to look for it sounds like this could be useful even with minimally "smart" feature selection.
- _lm_ 10y ago> 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 10y agoThe 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.
- madengr 10y agoWould be nice in an oscilloscope, though many have a good set of waveform measurements.
- placebo 10y agoI'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-noisy-curve/ http://nocurve.com/2014/01/12/finding-peaks-and-troughs-in-a...
- zump 10y agoWhy not use an RNN?
- MaxBenChrist 10y agowhat for? for the feature extraction part?