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

Useful links

• Paper
• Documentation
• Example (machine learning) notebooks

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!

Feature extraction

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

1. Load sequence-indexed data (in this case a time-index)

dftmp, dfacc, dfibi = loadempatica_data(['tmp', 'acc', 'ibi'])

2. Construct your feature extraction configuration

fc = FeatureCollection( MultipleFeatureDescriptors( functions=[np.min, np.mean, np.std, ss.skew, ss.kurtosis], seriesnames=["TMP", "ACCx", "ACC_y", "IBI"], windows=["15min", "30min"], strides="15min", ) )

3. Extract features

fc.calculate(data=[dftmp, dfacc, dfibi], approvesparsity=True) ```

Note that the feature extraction is performed on multivariate data with varying sample rates. | signal | columns | sample rate | |:-------|:-------|------------------:| | dftmp | ["TMP"]| 4Hz | | dfacc | ["ACCx", "ACCy", "ACCz" ]| 32Hz | | dfibi | ["IBI"]| irregularly sampled |

Processing

Working example in our docs

Why tsflex? ✨

• Flexible:
  • handles multivariate/multimodal time series
  • versatile function support => integrates with many packages for:
  • processing (e.g., scipy.signal, statsmodels.tsa)
  • feature extraction (e.g., numpy, scipy.stats, antropy, nolds, seglearn¹, tsfresh¹, tsfel¹)
  • feature extraction handles multiple strides & window sizes
• Efficient:
  
  • view-based operations for processing & feature extraction => extremely low memory peak & fast execution time
    
  • see: feature extraction benchmark visualization
• 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:
  • apply FeatureCollection.reduce after feature selection for faster inference
  • use function execution time logging to discover processing and feature extraction bottlenecks
  • embedded SeriesPipeline & FeatureCollection serialization
  • time series chunking

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:

bibtex @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