Hey, great talk! Hope you could answer this: Would these features extraction still be relevant for a regression task? I mean, not "auto"regression (forecasting) but rather, based on one or multiple timeseries, there is a single scalar output that needs to be predicted. So basically I want to use TSFRESH to extract features, and use those features in a standard regression setting to predict a target value (linear regression, XGBoost regression, etc.) Would this still make sense to use?
That would be unsupervised feature selection. That is a use-case which is not implemented in tsfresh, we have some (small) discussion here: github.com/blue-yonder/tsfresh/discussions/861
Nice talk.. Which technique is best for feature selection in case of unsupervised learning or clustering of time-series using feature extraction approach with tsfresh.
Not directly. Feature selection is about finding which features are relevant to solve a given problem (e.g. predicting the next time series value or classifying some data). Of course, it could be one step in your anomaly detection pipeline.
How can I utilize tsfresh to generate features of my univariate sales time series? e.g. date id units_sold 15-01-23 1 34.0 16-01-23 1 43.0 17-01-23 1 19.0 where 'id' simbolize ID of the product, in this case, my time series belong only to a single product... I did manage to obtain lots of features with 'extract_features' method, but then when I tried to 'extract_relevant_features' or 'select_features' I was unable to go any further since I do not have a 'y' pandas series. Is anyone facing the same challenge? thx for your help.
Nice talk, nice library, very odd room....
Nice talk. QA session
Hey, great talk!
Hope you could answer this:
Would these features extraction still be relevant for a regression task? I mean, not "auto"regression (forecasting) but rather, based on one or multiple timeseries, there is a single scalar output that needs to be predicted. So basically I want to use TSFRESH to extract features, and use those features in a standard regression setting to predict a target value (linear regression, XGBoost regression, etc.)
Would this still make sense to use?
I'm looking for the same answer. Trying to use tsfresh for a regression task. Could you let me know if you found an answer
@@kishore961 Hey! I am looking for the same answer. Did you find anything?
This is great. can you tell me, if you have a univariate time series, how would you perform feature selection as you have no Y values ?
That would be unsupervised feature selection. That is a use-case which is not implemented in tsfresh, we have some (small) discussion here: github.com/blue-yonder/tsfresh/discussions/861
Nice talk.. Which technique is best for feature selection in case of unsupervised learning or clustering of time-series using feature extraction approach with tsfresh.
You can look for "sequential clustering", and the MASS technique. github.com/matrix-profile-foundation/mass-ts
We have also some discussion here: github.com/blue-yonder/tsfresh/discussions/861
Is feature selection more or less equivalent to anomaly detection?
Not directly. Feature selection is about finding which features are relevant to solve a given problem (e.g. predicting the next time series value or classifying some data). Of course, it could be one step in your anomaly detection pipeline.
Is it possible to use the library with non-binary y values? In my case optimally 9 respectively a 3x3 different classes?
Yes, that is possible. tsfresh supports multi-class feature selection as well.
How can I utilize tsfresh to generate features of my univariate sales time series? e.g.
date id units_sold
15-01-23 1 34.0
16-01-23 1 43.0
17-01-23 1 19.0
where 'id' simbolize ID of the product, in this case, my time series belong only to a single product...
I did manage to obtain lots of features with 'extract_features' method, but then when I tried to 'extract_relevant_features' or 'select_features' I was unable to go any further since I do not have a 'y' pandas series.
Is anyone facing the same challenge? thx for your help.
Does your dataset only contain sales for 1 product?
this guy is pretty funny the crowed is so dead
where can i download code?
You can find the code here: github.com/blue-yonder/tsfresh
@@nilsbraun5266 thks!
quiet room but it is in Germany so...
Actually the stock example is a pretty bad example
5:15