NDLI: A Supervised Time Series Feature Extraction Technique Using DCT and DWT

Content Provider	IEEE Xplore Digital Library
Author	Batal, I. Hauskrecht, M.
Copyright Year	2009
Abstract	The increased availability of time series datasets prompts the development of new tools and methods that allow machine learning classifiers to better cope with time series data. Time series data are usually characterized by a high space dimensionality and a very strong correlation among features. This special nature makes the development of effective time series classifiers a challenging task. This work proposes and analyzes methods combining spectral decomposition and feature selection for time series classification problems and compares them against methods that work with original time series and time-dependent features. Briefly, our approach first applies discrete cosine transform (DCT) or discrete wavelet transform (DWT) on time series data. Then, it performs supervised feature selection/reduction by selecting only the most discriminative set of coefficients to represent the data. Experimental evaluations, carried out on multiple datasets, demonstrate the benefits of our approach in learning efficient and accurate time series classifiers.
Starting Page	735
Ending Page	739
File Size	272436
Page Count	5
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	DWT Time series analysis Time Series classification KNN Discrete wavelet transforms SVM Spectral features Application software Spectral analysis Support vector machines Computer science DFT DCT feature extraction Support vector machine classification Machine learning Feature extraction Discrete cosine transforms feature selection
Content Type	Text
Resource Type	Article

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