Article

An efficient and accurate method for evaluating time series similarity

Authors:

Michael D. Morse,

Jignesh M. PatelAuthors Info & Claims

SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

Pages 569 - 580

https://doi.org/10.1145/1247480.1247544

Published: 11 June 2007 Publication History

Abstract

A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specified ε threshold value, such as the LCSS and the EDR techniques, have been shown to be robust in the presence of noise, time shifts, and data scaling. Our work proposes a new algorithm, called the Fast Time Series Evaluation (FTSE) method, which can be used to evaluate such threshold value techniques, including LCSS and EDR. Using FTSE, we show that these techniques can be evaluated faster than using either traditional dynamic programming or even warp-restricting methods such as the Sakoe-Chiba band and the Itakura Parallelogram.

We also show that FTSE can be used in a framework that can evaluate a richer range of ε threshold-based scoring techniques, of which EDR and LCSS are just two examples. This framework, called Swale, extends the ε threshold-based scoring techniques to include arbitrary match rewards and gap penalties. Through extensive empirical evaluation, we show that Swale can obtain greater accuracy than existing methods.

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Index Terms

An efficient and accurate method for evaluating time series similarity
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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cover image ACM Conferences

SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

June 2007

1210 pages

ISBN:9781595936868

DOI:10.1145/1247480

General Chairs:
Lizhu Zhou
Tsinghua University, China
,
Tok Wang Ling
National University of Singapore, Singapore
,
Program Chair:
Beng Chin Ooi
National University of Singapore, Singapore

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 June 2007

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SIGMOD/PODS07

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SIGMOD/PODS07: International Conference on Management of Data

June 11 - 14, 2007

Beijing, China

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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https://doi.org/10.3390/app15031331
Mohammed JBöhlen MHelmer SBaeza-Yates RBonchi F(2024)Quantifying and Estimating the Predictability Upper Bound of Univariate Numeric Time SeriesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671995(2236-2247)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671995
Atanasovska AZdraveski VSpasov D(2024)Parallelizing Dynamic Time Warping Algorithm for Hotel Occupancy Time Series Similarity Measurement2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569678(1974-1978)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569678
Lee JKim DSim S(2024)Temporal Multi-Features Representation Learning-Based Clustering for Time-Series DataIEEE Access10.1109/ACCESS.2024.341734812(87675-87690)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417348
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