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