Library for time series subsequence search
Project description
Time Series Subsequence Search Library
Intuitive time series subsequence search
This repository hosts the TSSEARCH - Time Series Subsequence Search Python package. TSSEARCH assists researchers in exploratory analysis for query search and time series segmentation without requiring significant programming effort.
Functionalities
- Search: We provide methods for time series query search and segmentation
- Weights: The relative contribution of each point of the query to the overall distance can be expressed using a user-defined weight vector.
- Visualization: We provide visualizations to present the results of the segmentation and query search
- Unit tested: we provide unit tests for each feature
- Easily extended: adding new distances is easy, and we encourage you to contribute with your custom distances or search methods
Get started
Installation
TSSEARCH is available at PyPI:
pip install tssearch
Example
The code below segments a 10 s electrocardiography record:
import tssearch
# Load the query, (optional) weight vector and sequence
data = tssearch.load_ecg_example()
# Selects the Dynamic Time Warping (DTW) as the distance for the segmentation
cfg = tssearch.get_distance_dict(["Dynamic Time Warping"])
# Performs the segmentation
out = tssearch.time_series_segmentation(cfg, data['query'], data['sequence'], data['weight'])
Documentation
The documentation is available here.
Available distances
Lockstep |
---|
Lp Distances |
Pearson Correlation Distance |
Short Time Series Distance (STS) |
Elastic |
---|
Dynamic Time Warping (DTW) |
Longest Common Subsequence (LCSS) |
Time Warp Edit Distance (TWED) |
Time |
---|
Time Alignment Measurement (TAM) |
Acknowledgements
This work was supported by the project Geolocation non-Assisted by GPS for Mobile Networks in Indoor and Outdoor Environment (GARMIO), co-funded by Portugal 2020, framed through the COMPETE 2020 (Operational Programme Competitiveness and Internationalization) and European Regional Development Fund (ERDF) from European Union (EU) under Grant POCI-01-0247-FEDER-033479.
Project details
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