Visualet: Visualizing shapelets for time series classification

Li, Guozhong; Choi, Byron; Bhowmick, Sourav S; Wong, Grace Lai-Hung; Chun, Kwok Pan; Li, Shiwen

Abstract

Time series classification (TSC) has attracted considerable attention from both academia and industry. TSC methods that are based on shapelets (intuitively, small highly-discriminative subsequences have been found effective and are particularly known for their interpretability, as shapelets themselves are subsequences. A recent work has significantly improved the efficiency of shapelet discovery. For instance, the shapelets of more than 65% of the datasets in the UCR Archive (containing data from different application domains) can be computed within an hour, whereas those of 12 datasets can be computed within a minute. Such efficiency has made it possible for demo attendees to interact with shapelet discovery and explore high-quality shapelets. In this demo, we present Visualet - a tool for visualizing shapelets, and exploring effective and interpretable ones.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: International Conference on Information and Knowledge Management
• Start Date: Oct 19, 2020
• Acceptance Date: May 5, 2020
• Online Publication Date: Oct 19, 2020
• Publication Date: Oct 19, 2020
• Deposit Date: Jan 19, 2022
• Publisher: Association for Computing Machinery (ACM)
• Pages: 3429-3432
• Book Title: Proceedings of the 29th ACM International Conference on Information \& Knowledge Management
• ISBN: 9781450368599
• DOI: https://doi.org/10.1145/3340531.3417414
• Public URL: https://uwe-repository.worktribe.com/output/8545857