tsflex

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!

Feature extraction

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

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:

@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

Metadata

Release files for tsflex 0.4.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tsflex 0.4.1
File	Size	Uploaded
tsflex-0.4.1.tar.gz	59.4 kB	Sep 6, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for tsflex 0.4.1
File	Interpreter	ABI	Platform	Reset
tsflex-0.4.1-py3-none-any.whl 67.0 kB Sep 6, 2024	Python 3	none	any	Details

Total release size: 126.3 kB

Release files / tsflex-0.4.1.tar.gz

Download URL	tsflex-0.4.1.tar.gz
Size	59.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`ce5d5db6725a5c5748e543836acdf2a5187e0777a100dc637765ad4d4fa67988`
BLAKE2b-256 checksum How to use checksums	`a0fa67d34578971b50b2465b5cb9870c0ab5beb27e39a7de66337066670ba721`
Upload date	Sep 6, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.7.0 CPython/3.10.6 Linux/5.15.0-119-generic`

Release files / tsflex-0.4.1-py3-none-any.whl

Download URL	tsflex-0.4.1-py3-none-any.whl
Size	67.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c6606ec13ca4757bad6582ab2ded0f3743041c4a495b0686187f0707aa2dd6c5`
BLAKE2b-256 checksum How to use checksums	`16c2ab19fb54b574712476e0f5ce755b6b2987c974a4f831396118d307ccdfed`
Upload date	Sep 6, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.7.0 CPython/3.10.6 Linux/5.15.0-119-generic`