Discrete Wavelet Transform-Based Time Series Analysis and Mining

被引：154

作者：

Chaovalit, Pimwadee ^{[1
]}

Gangopadhyay, Aryya ^{[2
]}

Karabatis, George ^{[2
]}

Chen, Zhiyuan ^{[2
]}

机构：

[1] Natl Sci & Technol Dev Agcy, Klongluang 12120, Pathum Thani, Thailand

[2] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21250 USA

来源：

ACM COMPUTING SURVEYS | 2011年 / 43卷 / 02期

关键词：

Algorithms; Experimentation; Measurement; Performance; Classification; clustering; anomaly detection; similarity search; prediction; data transformation; dimensionality reduction; noise filtering; data compression; CLASSIFICATION; TOOL; PREDICTION; HOSPITALIZATION; MODELS; NEED;

D O I：

10.1145/1883612.1883613

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页数：37

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