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arXiv · 2111.10309

Unsupervised Visual Time-Series Representation Learning and Clustering

Gaurangi Anand·Richi Nayak
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Abstract

Time-series data is generated ubiquitously from Internet-of-Things (IoT) infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications, in enormous volumes. This paper investigates the potential of unsupervised representation learning for these time-series. In this paper, we use a novel data transformation along with novel unsupervised learning regime to transfer the learning from other domains to time-series where the former have extensive models heavily trained on very large labelled datasets. We conduct extensive experiments to demonstrate the potential of the proposed approach through time-series clustering.

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BibTeXRIS

Gaurangi Anand, Richi Nayak. 2021-11-19. Unsupervised Visual Time-Series Representation Learning and Clustering. https://doi.org/10.1007/978-3-030-63823-8_94

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