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tsflex

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tsflex is a toolkit for flexible time series processing & feature extraction, that is efficient and makes few assumptions about sequence data.

Useful links

Installation

command
pip pip install tsflex
conda conda install -c conda-forge tsflex

Usage

tsflex is built to be intuitive, so we encourage you to copy-paste this code and toy with some parameters!

import pandas as pd; import numpy as np; import scipy.stats as ss
from tsflex.features import MultipleFeatureDescriptors, FeatureCollection
from tsflex.utils.data import load_empatica_data

# 1. Load sequence-indexed data (in this case a time-index)
df_tmp, df_acc, df_ibi = load_empatica_data(['tmp', 'acc', 'ibi'])

# 2. Construct your feature extraction configuration
fc = FeatureCollection(
    MultipleFeatureDescriptors(
          functions=[np.min, np.mean, np.std, ss.skew, ss.kurtosis],
          series_names=["TMP", "ACC_x", "ACC_y", "IBI"],
          windows=["15min", "30min"],
          strides="15min",
    )
)

# 3. Extract features
fc.calculate(data=[df_tmp, df_acc, df_ibi], approve_sparsity=True)

Note that the feature extraction is performed on multivariate data with varying sample rates.

signal columns sample rate
df_tmp ["TMP"] 4Hz
df_acc ["ACC_x", "ACC_y", "ACC_z" ] 32Hz
df_ibi ["IBI"] irregularly sampled

Working example in our docs

Why tsflex? ✨

  • Flexible:
  • Efficient:
  • Intuitive:
    • maintains the sequence-index of the data
    • feature extraction constructs interpretable output column names
    • intuitive API
  • Few assumptions about the sequence data:
    • no assumptions about sampling rate
    • able to deal with multivariate asynchronous data
      i.e. data with small time-offsets between the modalities
  • Advanced functionalities:

¹ These integrations are shown in integration-example notebooks.

Future work 🔨

  • scikit-learn integration for both processing and feature extraction
    note: is actively developed upon sklearn integration branch.
  • Support time series segmentation (exposing under the hood strided-rolling functionality) - see this issue
  • Support for multi-indexed dataframes

=> Also see the enhancement issues

Contributing 👪

We are thrilled to see your contributions to further enhance tsflex.
See this guide for more instructions on how to contribute.

Referencing our package

If you use tsflex in a scientific publication, we would highly appreciate citing us as:

@article{vanderdonckt2021tsflex,
    author = {Van Der Donckt, Jonas and Van Der Donckt, Jeroen and Deprost, Emiel and Van Hoecke, Sofie},
    title = {tsflex: flexible time series processing \& feature extraction},
    journal = {SoftwareX},
    year = {2021},
    url = {https://github.com/predict-idlab/tsflex},
    publisher={Elsevier}
}

Link to the paper: https://www.sciencedirect.com/science/article/pii/S2352711021001904


👤 Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost