PaTSEmb

Welcome to PaTSEmb, a fast and extendable Python package for creating a pattern-based embedding of the time series. This is an embedding of the time series which contains information about the typical shapes are occurring at which locations in the time series. Below, we give a small example of how to do this, but be sure to check out the documentation!

Installation

You can install PaTSEmb using the following command:

pip install patsemb

If you want to mine frequent, sequential patterns, Java 1.7 or higher should also be available on your machine. More information about installing PaTSEmb can be found in the documentation.

Example

The code snippet below shows how to create the pattern-based embedding of a time series. Be sure to check out the example notebook for more examples!

from patsemb.discretization import SAXDiscretizer
from patsemb.pattern_mining import QCSP
from patsemb.pattern_based_embedding import PatternBasedEmbedder

# Specify a discretizer and pattern miner, or use the default values
pattern_based_embedder = PatternBasedEmbedder(
    discretizer=SAXDiscretizer(alphabet_size=8, word_size=5),
    pattern_miner=QCSP(minimum_support=3, top_k_patterns=20)
)

# Create the pattern-based embedding
time_series = ...  # Load here your time series as a numpy array
embedding = pattern_based_embedder.fit_transform(time_series)

Contact

Feel free to email to louis.carpentier@kuleuven.be if there are any questions, remarks, ideas, ...

Acknowledgments

If you use PaTSEmb in your research or project, please add the following citation:

@inproceedings{carpentier2024pattern,
    title={Pattern-based Time Series Semantic Segmentation with Gradual State Transitions},
    author={Carpentier, Louis and Feremans, Len and Meert, Wannes and Verbeke, Mathias},
    booktitle={Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)},
    pages={316--324},
    year={2024},
    month={April},
    organization={SIAM},
    doi={10.1137/1.9781611978032.36}
}

L. Carpentier, L. Feremans, W. Meert, and M. Verbeke. "Pattern-based time series semantic segmentation with gradual state transitions". In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), pages 316–324. SIAM, april 2024. doi: 10.1137/1.9781611978032.36.

License

Copyright (c) 2024 KU Leuven, DTAI Research Group

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.