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Nonlinear time series classification using bispectrum‐based deep convolutional neural networks
Author(s) -
Parker Paul A.,
Holan Scott H.,
Ravishanker Nalini
Publication year - 2020
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2536
Subject(s) - bispectrum , computer science , convolutional neural network , time series , series (stratigraphy) , big data , nonlinear system , process (computing) , artificial intelligence , machine learning , data mining , artificial neural network , time domain , deep learning , data science , econometrics , spectral density , mathematics , telecommunications , paleontology , physics , quantum mechanics , computer vision , biology , operating system
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.