An Efficient Content-based Time Series Retrieval System-Reference-Cited by-同舟云学术

An Efficient Content-based Time Series Retrieval System

Published:2023-10-21 Issue: Volume: Page:
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Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Yeh Chin-Chia Michael¹^ORCID,Chen Huiyuan¹^ORCID,Dai Xin¹^ORCID,Zheng Yan²^ORCID,Wang Junpeng¹^ORCID,Lai Vivian¹^ORCID,Fan Yujie¹^ORCID,Der Audrey³^ORCID,Zhuang Zhongfang¹^ORCID,Wang Liang¹^ORCID,Zhang Wei¹^ORCID,Phillips Jeff M.⁴^ORCID

Affiliation:

1. Visa Research, Palo Alto, CA, USA

2. Visa Resarch, Palo Alto, CA, USA

3. University of California, Riverside, Riverside, CA, USA

4. University of Utah, Salt Lake City, UT, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3614655

Reference46 articles.

1. Alexandr Andoni and Daniel Beaglehole . 2022 . Learning to hash robustly, guaranteed . In International Conference on Machine Learning. PMLR, 599--618 . Alexandr Andoni and Daniel Beaglehole. 2022. Learning to hash robustly, guaranteed. In International Conference on Machine Learning. PMLR, 599--618.

2. Anthony Bagnall , Jason Lines , Aaron Bostrom, James Large, and Eamonn Keogh. 2017 . The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, Vol. 31 , 3 (2017), 606--660. Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh. 2017. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, Vol. 31, 3 (2017), 606--660.

3. iSAX 2.0: Indexing and Mining One Billion Time Series

4. TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

5. Denoising Self-Attentive Sequential Recommendation