Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching

Author:

Kegel Lars,Hartmann ClaudioORCID,Thiele Maik,Lehner Wolfgang

Abstract

AbstractProcessing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.

Funder

Technische Universität Dresden

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference18 articles.

1. Agrawal R, Faloutsos C, Swami A (1993) Efficient similarity search in sequence databases. FODO 730:69–84

2. Butler M, Kazakov D (2015) SAX discretization does not guarantee equiprobable symbols. IKDE 27(4):1162–1166. https://doi.org/10.1109/TKDE.2014.2382882

3. Chen Q, Chen L, Lian X, Liu Y, Yu JX (2007) Indexable PLA for efficient similarity search. In: Proc. of VLDB, pp 435–446

4. Kendall MG, Stuart A (1983) The advanced theory of statistics vol 3. Griffin, , pp 410–414

5. Lin J, Keogh EJ, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Workshop Proc. of SIGMOD, pp 2–11 https://doi.org/10.1145/882082.882086

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm;Mathematical and Computational Applications;2024-09-11

2. Use of a Surrogate Model for Symbolic Discretization of Temporal Data Sets Through eMODiTS and a Training Set with Varying-Sized Instances;Lecture Notes in Computer Science;2024

3. Mining Seasonal Temporal Patterns in Time Series;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04

4. A comparative study on recognizing human activities by applying diverse Machine Learning approaches;2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2022-07-11

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3