Toolkit for flexible processing & feature extraction on time-series data
Project description
tsflex is a toolkit for flexible time-series processing & feature extraction, making few assumptions about input data.
Useful links
Installation
If you are using pip, just execute the following command:
pip install tsflex
Why tsflex? ✨
- flexible;
- handles multi-variate time-series
- versatile function support
=> integrates natively with many packages for processing (e.g., scipy.signal) & 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
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 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)
More examples
For processing look here
Other examples can be found here
Future work 🔨
- 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.
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 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
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