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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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Cited By

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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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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Published In

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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Publication History

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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Cited By

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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
Braig MZeiler P(2024)A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering SystemsIEEE Access10.1109/ACCESS.2024.338469712(49602-49623)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3384697
Naser ANaser M(2024)SPINEX-TimeSeries: Similarity-based predictions with explainable neighbors exploration for time series and forecasting problemsComputers & Industrial Engineering10.1016/j.cie.2024.110812(110812)Online publication date: Dec-2024
https://doi.org/10.1016/j.cie.2024.110812
Tanimu BHamed MBello AAbdullahi SAjibike MShahid S(2024)Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithmsEnvironmental Science and Pollution Research10.1007/s11356-024-32128-0Online publication date: 3-Feb-2024
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Paparrizos JWu KElmore AFaloutsos CFranklin M(2023)Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding DistancesProceedings of the VLDB Endowment10.14778/3594512.359453016:8(2019-2032)Online publication date: 22-Jun-2023
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Chakraborty RHolm JPedersen TPopovski P(2023)Finding Representative Sampling Subsets in Sensor Graphs Using Time-series SimilaritiesACM Transactions on Sensor Networks10.1145/359518119:4(1-32)Online publication date: 9-Jun-2023
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Novokhrestova DKostyuchenko EBorovskoy A(2023)Classifier-Based Combined Measure of Syllable Pronunciation Similarity in Speech RehabilitationProceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23)10.1007/978-3-031-43792-2_13(127-136)Online publication date: 18-Sep-2023
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Alghamdi NZhang LRundensteiner EEltabakh MIves ZBonifati AEl Abbadi A(2022)Scalable Time Series Compound InfrastructureProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517888(1685-1698)Online publication date: 10-Jun-2022
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