Skip to content

Latest commit



159 lines (106 loc) · 5.19 KB


File metadata and controls

159 lines (106 loc) · 5.19 KB

Quick Start

Install tsfresh

As the compiled tsfresh package is hosted on the Python Package Index (PyPI) you can easily install it with pip

pip install tsfresh

If you need to work with large time series data that may not fit in memory, install tsfresh with Dask:

pip install tsfresh[dask]

See also :ref:`large-data-label`.

Dive in

Before boring yourself by reading the docs in detail, you can dive right into tsfresh with the following example:

We are given a data set containing robot failures as discussed in [1]. Each robot records time series from six different sensors. For each sample denoted by a different id we are going to classify if the robot reports a failure or not. From a machine learning point of view, our goal is to classify each group of time series.

To start, we load the data into python

from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, \
timeseries, y = load_robot_execution_failures()

and end up with a pandas.DataFrame timeseries having the following shape

  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
... ... ... ... ... ... ... ... ...

The first column is the DataFrame index and has no meaning here. There are six different time series (F_x, F_y, F_z, T_x, T_y, T_z) for the different sensors. The different robots are denoted by the ids column.

On the other hand, y contains the information which robot id reported a failure and which not:

1 0
2 0
3 0
4 0
5 0
... ...

Here, for the samples with ids 1 to 5 no failure was reported.

In the following we illustrate the time series of the sample id 3 reporting no failure:

import matplotlib.pyplot as plt
timeseries[timeseries['id'] == 3].plot(subplots=True, sharex=True, figsize=(10,10))

the time series for id 3 (no failure)

And for id 20 reporting a failure:

timeseries[timeseries['id'] == 20].plot(subplots=True, sharex=True, figsize=(10,10))

the time series for id 20 (failure)

You can already see some differences by eye - but for successful machine learning we have to put these differences into numbers.

For this, tsfresh comes into place. It allows us to automatically extract over 1200 features from those six different time series for each robot.

For extracting all features, we do:

from tsfresh import extract_features
extracted_features = extract_features(timeseries, column_id="id", column_sort="time")

You end up with the DataFrame extracted_features with more than 1200 different extracted features. We will now first, remove all NaN values (which were created by feature calculators that can not be used on the given data, e.g., because the statistics are too low), and then select only the relevant features:

from tsfresh import select_features
from tsfresh.utilities.dataframe_functions import impute

features_filtered = select_features(extracted_features, y)

Only around 300 features were classified as relevant enough.

Further, you can even perform the extraction, imputing and filtering at the same time with the :func:`tsfresh.extract_relevant_features` function:

from tsfresh import extract_relevant_features

features_filtered_direct = extract_relevant_features(timeseries, y,
                                                     column_id='id', column_sort='time')

You can now use the features in the DataFrame features_filtered (which is equal to features_filtered_direct) in conjunction with y to train your classification model. You can find an example in the Jupyter notebook 01 Feature Extraction and Selection.ipynb where we train a RandomForestClassifier using the extracted features.