[prostodata]

In fact, there are two general approaches to unifying ML with time series:

o Unifying ML API and patterns so that time series can be analyzed in the same way as normal tabular data. Example: sktime

o Preprocessing libraries applying data transformations to the input time series by converting them to a (tabular) form which can be used by normal ML algorithms. These are typically general or specific libraries for feature engineering, feature learning, feature selection and generic transformations. Examples: https://github.com/blue-yonder/tsfresh or https://github.com/prostodata/prosto

Which approach will win is not clear.

[uoaei]

My experience has been that if you are doing feature engineering and using summary vectors for time series classification, rather than algorithms which deal with time series directly, and it's working, the problem is not really complicated enough to need high-compute machine learning methods and you'll be fine with one of the more popular off-the-shelf methods.

[mloning]

We're actually interfacing tsfresh. Unifying ML with time series is perhaps better understood in terms of the different learning tasks (e.g. time series classification/regression/clustering, forecasting, time series annotation) and their relations (applying algorithms for one task to help solve another).

[hcrisp]

Sktime seems similar to DARTS, which includes both preprocessing and modeling patterns.

https://medium.com/unit8-machine-learning-publication/darts-...

[nerdponx]

Sktime is more like "Scikit-learn for time series data", which notably lacks any time series methods.