Data scientists spend much of their time cleaning data or building features. While the former is unavoidable, the latter can be automated.

tsfresh uses a robust feature selection algorithm to automatically extract relevant time series features, freeing up data scientists’ time.

To demonstrate this, start with loading an example dataset:

from tsfresh.examples.robot_execution_failures import (
    download_robot_execution_failures,
    load_robot_execution_failures,
)

download_robot_execution_failures()
timeseries, y = load_robot_execution_failures()
timeseries.head()

Output:

  id  time  F_x  F_y  F_z  T_x  T_y  T_z
0   1     0   -1   -1   63   -3   -1    0
1   1     1    0    0   62   -3   -1    0
2   1     2   -1   -1   61   -3    0    0
3   1     3   -1   -1   63   -2   -1    0
4   1     4   -1   -1   63   -3   -1    0

Extract features and select only relevant features for each time series.

from tsfresh import extract_relevant_features

# extract relevant features
features_filtered = extract_relevant_features(
    timeseries, y, column_id="id", column_sort="time"
)

You can now use the features in features_filtered to train your classification model.

Link to tsfresh.

Interact with this code in Google Colab.