title: Python, extracting features form time series (TSFRESH package or what can I use?)
body: <p>I need some help for feature extraction in time series, maybe using the TSFRESH package.</p>
  <p>I have circa 5000 CSV files, and each one of them is a single time series (they may differ in length). The CSV-time-series is pretty straight forward:</p>
  <p>Example of a CSV-Time-Series file:
  |  Date  | Value |
  | ------ | ----- |
  | 1/1/1904  01:00:00,000000 | 1,464844E-3 |
  | 1/1/1904  01:00:01,000000 | 1,953125E-3 |
  | 1/1/1904  01:00:02,000000 | 4,882813E-4 |
  | 1/1/1904  01:00:03,000000 | -2,441406E-3 |
  | 1/1/1904  01:00:04,000000 | -9,765625E-4 |
  | ...    |     ...      |</p>
  <p>Along with these CSV files, I also have a metadata file (in a CSV format), where each row refers to one of those 5000 CSV-time-series, and reports more general information about that time series such as the energy, etc.</p>
  <p>Example of the metadata-CSV file:
  |  Path of the CSV-timeseries     | Label   |  Energy      | Penetration |  Porosity   |
  | ------ | -----        | ------ | -----        | -----       | ----------- |
  | ...    |     ...      | ...    |     ...      |     ...     |     ...     |
  | ...    |     ...      | ...    |     ...      |     ...     |     ...     |
  | ...    |     ...      | ...    |     ...      |     ...     |     ...     |</p>
  <p>The most important column is the &quot;Label&quot; one since it reports if a CSV-time-series was labeled as:</p>
  <ol>
  <li>Good</li>
  <li>Bad</li>
  </ol>
  <p>I should also consider the energy, penetration, and porosity columns since those values have a big role in the labeling of the time series. (I already tried a decision tree by looking at only the features, now I would like to analyze the time series to extract knowledge)</p>
  <p>I intend to extract features from the time series such that I can understand what are the features that make one time series be labeled as &quot;Good&quot; or &quot;Bad&quot;.</p>
  <p>How can I do this with TSFRESH?
  There are other ways to do this?</p>
  <p>Could you show me how to do it? Thank you :)</p>