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

• Documentation
• Example notebooks

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

• flexible;
  • handles multivariate 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)
  • 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

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 scipy.stats as ssig; import numpy as np
from tsflex.features import FeatureDescriptor, FeatureCollection, NumpyFuncWrapper

# 1. -------- Get your time-indexed data --------
# Data contains 1 column; ["TMP"]
url = "https://github.com/predict-idlab/tsflex/raw/main/examples/data/empatica/tmp.parquet"
data = pd.read_parquet(url).set_index("timestamp")

# 2 -------- Construct your feature collection --------
fc = FeatureCollection(
    feature_descriptors=[
        FeatureDescriptor(
            function=NumpyFuncWrapper(func=ssig.skew, output_names="skew"),
            series_name="TMP", 
            window="5min",  # Use 5 minutes 
            stride="2.5min",  # With steps of 2.5 minutes
        )
    ]
)
# -- 2.1. Add features to your feature collection
fc.add(FeatureDescriptor(np.min, "TMP", '2.5min', '2.5min'))

# 3 -------- Calculate features --------
fc.calculate(data=data)

For processing look here
Other examples can be found here

• scikit-learn integration for both processing and feature extraction
  note: is actively developed upon sklearn integration branch.
• support for multi-indexed dataframes
• random-strided rolling for data-augmention purposes.

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}
}

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