research-article

Discrete wavelet transform-based time series analysis and mining

Authors:

Pimwadee Chaovalit,

Aryya Gangopadhyay,

George Karabatis,

Zhiyuan ChenAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 43, Issue 2

Article No.: 6, Pages 1 - 37

https://doi.org/10.1145/1883612.1883613

Published: 04 February 2011 Publication History

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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Zeira, G., Maimon, O., Last, M., and Rokach, L. 2004. Change detection in classification models induced from time series data. In Data Mining in Time Series Databases. World Scientific Publishing, Singapore, 101--125.

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Discrete wavelet transform-based time series analysis and mining

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 43, Issue 2

January 2011

276 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1883612

Issue’s Table of Contents

Copyright © 2011 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 February 2011

Accepted: 01 April 2009

Revised: 01 January 2009

Received: 01 September 2008

Published in CSUR Volume 43, Issue 2

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Massar HBelhoussine Drissi TNsiri BMiyara M(2025)Advancements in Blind Source Separation for EEG Artifact Removal: A comparative analysis of Variational Mode Decomposition and Discrete Wavelet Transform approachesApplied Acoustics10.1016/j.apacoust.2024.110300228(110300)Online publication date: Jan-2025
https://doi.org/10.1016/j.apacoust.2024.110300
Ranjan SSaini CSamir SGoel AKumar E(2024)Forecasting Indian Trade Trends through LSTM- based Predictive ModelingInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24APR664(1691-1700)Online publication date: 4-May-2024
https://doi.org/10.38124/ijisrt/IJISRT24APR664
Olofsson CToftaker IRachah AReksen OKielland C(2024)Pathogen-specific patterns of milking traits in automatic milking systemsJournal of Dairy Science10.3168/jds.2023-23933107:8(6035-6051)Online publication date: Aug-2024
https://doi.org/10.3168/jds.2023-23933
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Verma SSahu SSahu T(2024)Wavelet decomposition-based multi-stage feature engineering and optimized ensemble classifier for stock market predictionThe Engineering Economist10.1080/0013791X.2024.232852669:3(213-238)Online publication date: 20-Mar-2024
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Sarwar SAziz GWaheed RMorales L(2024)Forecasting the mineral resource rent through the inclusion of economy, environment and energy: Advanced machine learning and deep learning techniquesResources Policy10.1016/j.resourpol.2024.10472990(104729)Online publication date: Mar-2024
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