@inproceedings{4ee8e9f8219d4585a794ddccb4de7804,
title = "Visualet: Visualizing Shapelets for Time Series Classification",
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.",
keywords = "accuracy, efficiency, shapelet discovery, time-series classification",
author = "Guozhong Li and Choi, {Koon Kau} and Bhowmick, {Sourav S.} and Wong, {Grace Lai Hung} and Chun, {Kwok Pan} and Shiwen Li",
note = "Funding Information: This work has been supported by the Hong Kong Research Grant Council (HKRGC) 12201119, 12232716, 12201518, 12200817, and 12201018, and National Science Foundation of China (NSFC) 61602395.; 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
year = "2020",
month = oct,
day = "19",
doi = "10.1145/3340531.3417414",
language = "English",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery (ACM)",
pages = "3429--3432",
booktitle = "CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management",
address = "United States",
}