Nonlinear time series classification using bispectrum‐based deep convolutional neural networks

Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject‐domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of...

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Bibliographic Details
Published in:Applied stochastic models in business and industry Vol. 36; no. 5; pp. 877 - 890
Main Authors: Parker, Paul A., Holan, Scott H., Ravishanker, Nalini
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.09.2020
Subjects:
Artificial neural networks
Classification
deep learning
Energy consumption
higher‐order spectra
Linearity
neural network algorithms
Neural networks
nonlinear time series
Spectrum analysis
supervised learning
Time series
Trends
ISSN:1524-1904, 1526-4025
Online Access:Get full text
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Summary:Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject‐domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Google trends data, which exhibit highly nonlinear behavior. Although a rich literature exists for addressing this problem, existing approaches mostly rely on first‐ and second‐order properties of the time series, since they typically assume linearity of the underlying process. Often, these are inadequate for effective classification of nonlinear time series data such as Google Trends data. Given these methodological deficiencies and the abundance of nonlinear time series that persist among real‐world phenomena, we introduce an approach that merges higher order spectral analysis with deep convolutional neural networks for classifying time series. The effectiveness of our approach is illustrated using simulated data and two motivating industry examples that involve Google trends data and electronic device energy consumption data.
Bibliography:Funding information
Air Force Research Laboratory, 19C0067; National Science Foundation, SES‐1853096
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.2536