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Author Chaovalit, Pimwadee ♦ Gangopadhyay, Aryya ♦ Karabatis, George ♦ Chen, Zhiyuan
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Classification ♦ Anomaly detection ♦ Clustering ♦ Data compression ♦ Data transformation ♦ Dimensionality reduction ♦ Noise filtering ♦ Prediction ♦ Similarity search
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.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-02-04
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 43
Issue Number 2
Page Count 37
Starting Page 1
Ending Page 37


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Source: ACM Digital Library