Extract Features on Time Series Easily

Automatically extract hundreds of relevant features to solve your time series problem with ease

from tsfresh import extract_relevant_features
from tsfresh.examples.robot_execution_failures import load_robot_execution_failures

timeseries, y = load_robot_execution_failures()

features = extract_relevant_features(timeseries, y, column_id="id", column_sort="time")

Use Cases


Use the extracted relevant features to train your usual ML model to distinguish between different time series classes.


No need for complicated methods! With tsfresh your time series forecasting problem becomes a usual regression problem.

Outlier Detection

Detect interesting patterns and outliers in your time series data by clustering the extracted features or training an ML method on them.

tsfresh is the basis for your next time series project!

Extract Features Automatically

Do not waste time on feature engineering

tsfresh extracts features on your time series data simple and fast, so you can spend more time on using these features.

Use hundreds of field tested features

The feature library in tsfresh contains features calculators from multiple domains, so you can get the best out of your data

Extract Features Now

Install tsfresh via pip or conda.

$ pip install -U tsfresh
# or
$ conda install -c conda-forge tsfresh