FAQΒΆ

  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 conviniently create a rolled time series datframe 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: How to handle rolling time series.

  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:

    System Message: ERROR/3 (/home/docs/checkouts/readthedocs.org/user_builds/tsfresh/checkouts/v0.7.1/docs/text/faq.rst, line 28)

    Unexpected indentation.

    conda create -n ENV_NAME python=VERSION
conda install -n ENV_NAME pip requests numpy pandas scipy statsmodels patsy scikit-learn future six tqdm
activate ENV_NAME
pip install tsfresh

    Please be aware that we’re using multiprocessing, which can be problematic.