1. Does tsfresh support different time series lengths?

    Yes, it supports different time series lengths. However, some feature calculators can demand a minimal length of the time series. If a shorter time series is passed to the calculator, a NaN is returned for those features.

  2. Is it possible to extract features from rolling/shifted time series?

    Yes, the tsfresh.dataframe_functions.roll_time_series() function allows to conveniently create a rolled time series dataframe from your data. You just have to transform your data into one of the supported tsfresh Data Formats. Then, the tsfresh.dataframe_functions.roll_time_series() give you a DataFrame with the rolled time series, that you can pass to tsfresh. On the following page you can find a detailed description: Rolling/Time series forecasting.

  3. How can I use tsfresh with windows?

    We recommend to use Anaconda. After installing, open the Anaconda Prompt, create an environment and set up tsfresh (Please be aware that we’re using multiprocessing, which can be problematic.):

    conda create -n ENV_NAME python=VERSION
    conda install -n ENV_NAME pip requests numpy pandas scipy statsmodels patsy scikit-learn tqdm
    activate ENV_NAME
    pip install tsfresh
  4. Does tsfresh support different sampling rates in the time series?

    Yes! The feature calculators in tsfresh do not care about the sampling frequency. You will have to use the second input format, the stacked DataFramed (see Data Formats)