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Discrete Wavelet Transform-Based Time Series Analysis and Mining
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DOI：10.1145/1883612.1883613.png)
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Abstract
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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.
Keywords:
Algorithms
Experimentation
Measurement
Performance
Classification
clustering
anomaly detection
similarity search
prediction
data transformation
dimensionality reduction
noise filtering
data compression
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