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1. **Does tsfresh support different time series lengths? **
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Yes, it supports different time series lengths. However, some feature calculators can demand a minimal length
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of the time series. If a shorter time series is passed to the calculator, a NaN is returned for those
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features.
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2. **Is it possible to extract features from rolling/shifted time series? **
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Yes, the :func: `tsfresh.dataframe_functions.roll_time_series ` function allows to conviniently create a rolled
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time series datframe from your data. You just have to transform your data into one of the supported tsfresh
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:ref: `data-formats-label `.
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Then, the :func: `tsfresh.dataframe_functions.roll_time_series ` give you a DataFrame with the rolled time series,
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that you can pass to tsfresh.
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On the following page you can find a detailed description: :ref: `rolling-label `.
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+ 3. **How can I use tsfresh with windows? **
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+ We recommend to use `Anaconda <https://www.continuum.io/downloads#windows >`_. After installing, open the
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+ Anaconda Prompt, create an environment and set up tsfresh:
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+ conda create -n ENV_NAME python=VERSION
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+ conda install -n ENV_NAME pip requests numpy pandas scipy statsmodels patsy scikit-learn future six tqdm
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+ activate ENV_NAME
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+ pip install tsfresh
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+ Please be aware that we're using multiprocessing, which can be `problematic <http://stackoverflow.com/questions/18204782/runtimeerror-on-windows-trying-python-multiprocessing >`_.
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