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Deep Time-Series Clustering: A Review

Ali Alqahtani, Mohammed Ali, Xianghua Xie Orcid Logo, Mark Jones Orcid Logo

Electronics, Volume: 10, Issue: 23, Start page: 3001

Swansea University Authors: Ali Alqahtani, Mohammed Ali, Xianghua Xie Orcid Logo, Mark Jones Orcid Logo

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Abstract

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series da...

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Published in: Electronics
ISSN: 2079-9292
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58874
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Abstract: We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.
Keywords: deep learning; clustering; time series data
College: College of Science
Funders: Deanship of Scientific Research, King Khalid University of Kingdom of Saudi Arabia under research grant number (RGP1/207/42).
Issue: 23
Start Page: 3001