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Erschienen in:
Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Local Subsequence-Based Distribution for Time Series Clustering

verfasst von : Lei Gong, Hang Zhang, Zongyou Liu, Kai Ming Ting, Yang Cao, Ye Zhu

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

Analyzing the properties of subsequences within time series can reveal hidden patterns and improve the quality of time series clustering. However, most existing methods for subsequence analysis require point-to-point alignment, which is sensitive to shifts and noise. In this paper, we propose a clustering method named CTDS that treats time series as a set of independent and identically distributed (iid) points in \(\mathbb {R}^d\) extracted by a sliding window in local regions. CTDS utilises a distributional measure called Isolation Distributional Kernel (IDK) that can capture the subtle differences between probability distributions of subsequences without alignment. It has the ability to cluster large non-stationary and complex datasets. We evaluate CTDS on UCR time series benchmark datasets and demonstrate its superior performance than other state-of-the-art clustering methods.

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Vorheriges Kapitel AutoClues: Exploring Clustering Pipelines via AutoML and Diversification
Nächstes Kapitel Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model
Fußnoten
1
The source code is available at https://​github.​com/​LeisureGong/​CTDS.
 
2
The training and testing subsets of each dataset are merged for clustering evaluation.
 
3
For short time series data with length less than 150, we directly generate all subsequences without split the data into segments.
 
4
We also evaluated their performance using RI and found a similar result. All experimental details can be found in the supplementary file.
 
5
For methods that solely produce representations or kernel matrices, we use K-means or Kernel K-means as the clustering algorithm.
 
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Metadaten
Titel
Local Subsequence-Based Distribution for Time Series Clustering
verfasst von
Lei Gong
Hang Zhang
Zongyou Liu
Kai Ming Ting
Yang Cao
Ye Zhu
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-41-9

Electronic ISBN: 978-981-9722-42-6

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-2242-6_21

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