@inproceedings{10.1145/3340531.3417414,
author = {Li, Guozhong and Choi, Byron and Bhowmick, Sourav S. and Wong, Grace Lai-Hung and Chun, Kwok-Pan and Li, Shiwen},
title = {Visualet: Visualizing Shapelets for Time Series Classification},
year = {2020},
isbn = {9781450368599},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3340531.3417414},
doi = {10.1145/3340531.3417414},
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.},
booktitle = {Proceedings of the 29th ACM International Conference on Information & Knowledge Management},
pages = {3429–3432},
numpages = {4},
keywords = {efficiency, accuracy, time-series classification, shapelet discovery},
location = {Virtual Event, Ireland},
series = {CIKM '20}
}