Nonlinear time series classification using bispectrum‐based deep convolutional neural networks
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 05, 2020
- Source ID
- 10.1002/asmb.2536
Entities
People
- Nalini Ravishanker
- Paul A. Parker
- Scott H. Holan
Organizations
- Air Force Research Laboratory
- National Science Foundation
- United States Census Bureau
- University of Connecticut
- University of Missouri