# Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2003.02353 · HTML: https://mlsanity.com/paper/2003.02353/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Deep learning based sferics recognition for AMT data processing in the dead band](https://mlsanity.com/paper/2209.13647/) (2022)
- [Optimal decision making in robotic assembly and other trial-and-error tasks](https://mlsanity.com/paper/2301.10846/) (2023)

## Abstract (arXiv)

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 (HOSA) with deep convolutional neural networks (CNNs) 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.