tsflex 0.1.2.3

Toolkit for flexible processing & feature extraction on time-series data

tsflex is a toolkit for flexible time series processing & feature extraction, making few assumptions about input data.

Useful links

• Documentation
• Example notebooks

Installation

If you are using pip, just execute the following command:

pip install tsflex

Or, if you are using conda, then execute this command:

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

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

# 1. -------- Get your time-indexed data --------
url = "https://github.com/predict-idlab/tsflex/raw/main/examples/data/empatica/"
# Contains 1 column; ["TMP"] - 4 Hz sampling rate
data_tmp = pd.read_parquet(url+"tmp.parquet").set_index("timestamp")
# Contains 3 columns; ["ACC_x", "ACC_y", "ACC_z"] - 32 Hz sampling rate
data_acc = pd.read_parquet(url+"acc.parquet").set_index("timestamp")

# 2. -------- Construct your feature collection --------
fc = FeatureCollection(
    MultipleFeatureDescriptors(
          functions=[np.min, np.max, np.mean, np.std, np.median, ss.skew, ss.kurtosis],
          series_names=["TMP", "ACC_x", "ACC_y"], # Use 3 multimodal signals 
          windows=["5min", "7.5min"],  # Use 5 minutes and 7.5 minutes 
          strides="2.5min",  # With steps of 2.5 minutes
    )
)

# 3. -------- Calculate features --------
fc.calculate(data=[data_tmp, data_acc])

More examples

For processing look here
Other examples can be found here

Why tsflex? ✨

• flexible;
  • handles multivariate/multimodal time series
  • versatile function support
    => integrates natively with many packages for processing (e.g., scipy.signal, statsmodels.tsa) & feature extraction (e.g., numpy, scipy.stats, seglearn¹, tsfresh¹, tsfel¹)
  • feature-extraction handles multiple strides & window sizes
• efficient view-based operations
  => extremely low memory peak & fast execution times (see benchmarks)
• maintains the time-index of the data
• makes little to no assumptions about the time series data

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)
  note: see more here.
• support for multi-indexed dataframes

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}
}

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