Cited by 218Time Series Analysis and ForecastingMathematical Analysis and Transform MethodsData Mining Algorithms and Applications

Discrete Wavelet transform-based time series analysis and mining

Chaovalit, Pimwadee, Gangopadhyay, Aryya, Karabatis, George, Chen, Zhiyuan·2011·ACM Computing Surveys

Abstract

Time series are recorded values of an interesting phenomenon such as stock prices, household incomes, or patient heart rates over a period of time. Time series data mining focuses on discovering interesting patterns in such data. This article introduces a wavelet-based time series data analysis to interested readers. It provides a systematic survey of various analysis techniques that use discrete wavelet transformation (DWT) in time series data mining, and outlines the benefits of this approach demonstrated by previous studies performed on diverse application domains, including image classification, multimedia retrieval, and computer network anomaly detection.

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Crossref · 10.1145/1883612.1883613

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Authors
Chaovalit, P., Gangopadhyay, A., Karabatis, G., & Chen, Z.
Affiliations
  • National Science and Technology Development Agency (TH)
  • University of Maryland, Baltimore County (US)
Venue
ACM Computing Surveys
Published
2011-01
Volume
43
Issue
2
Pages
1-37
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
OpenAlex
W1971353559
Cited by
218
References
127
Citations by year
2026: 15 · 2025: 23 · 2024: 17 · 2023: 24 · 2022: 15 · 2021: 20 · 2020: 17 · 2019: 13 · 2018: 17 · 2017: 10 · 2016: 9 · 2015: 10 · 2014: 11 · 2013: 7 · 2012: 9
FWCI
12.85
Type
journal article
Language
en
Open access
closed
Locations
ACM Computing Surveys (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
  • Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
Keywords
discrete wavelet transform, time series data mining, time series analysis, pattern discovery, image classification, multimedia retrieval, network anomaly detection

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