BacktrackSTL: Ultra-Fast Online Seasonal-Trend Decomposition with Backtrack Technique-Reference-Cited by-同舟云学术

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BacktrackSTL: Ultra-Fast Online Seasonal-Trend Decomposition with Backtrack Technique

Published:2024-08-24 Issue: Volume:6 Page:5848-5859
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
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Author:

Wang Haoyu¹^ORCID,Guo Hongke²^ORCID,Zhu Zhaoliang¹^ORCID,Zhang You¹^ORCID,Zhou Yu²^ORCID,Zheng Xudong³^ORCID

Affiliation:

1. Alibaba Group, Hangzhou, Zhejiang, China

2. Alibaba Group, Beijing, China

3. Alibaba Group, Zhejiang, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671510

Reference39 articles.

1. 2023. Apache Flink. https://flink.apache.org/

2. 2023. Elastic Compute Service (ECS). https://www.alibabacloud.com/help/en/ ecs/

3. 2023. OneShotSTL. https://github.com/xiao-he/OneShotSTL

4. 2023. Realtime Compute for Apache Flink. https://www.alibabacloud.com/help/ en/flink/

5. 2023. Simple Log Service (SLS). https://www.alibabacloud.com/help/en/sls/

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