Unsupervised Visual Time-Series Representation Learning and Clustering

• URL: http://arxiv.org/abs/2111.10309v1
• Date: Fri, 19 Nov 2021 16:44:33 GMT
• Title: Unsupervised Visual Time-Series Representation Learning and Clustering
• Authors: Gaurangi Anand and Richi Nayak
• Abstract summary: Time-series data is generated ubiquitously from Internet-of-Things infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications. This paper investigates the potential of unsupervised representation learning for these time-series.
• Score: 2.610470075814367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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