Work

Structural classification of locally stationary time series based on second-order characteristics

Year: 2026

Type: article

Abstract: Time series classification is crucial for numerous scientific and engineering applications. In this article, we present a numerically efficient, practically competitive, and theoretically rigorous classification method for distinguishing between two classes of locally stationary time series based on their time-domain, second-order characteristics. Our approach builds on the autoregressive approximation for locally stationary time series, imposes no requirement on the training sample size, and is shown to achieve zero misclassification error rate asymptotically when the underlying time series differ only mildly in their second-order characteristics. The new method is demonstrated to outperform a variety of state-of-the-art solutions, including wavelet-based, tree-based, convolution-based methods, as well as modern deep learning methods, through intensive numerical simulations and a real electroencephalography data analysis for epilepsy classification.

Source: Journal of the Royal Statistical Society Series B (Statistical Methodology)

DOI: 10.1093/jrsssb/qkag083

Authors: Chen Qian, Xiucai Ding, Lexin Li

Institutions: University of California, Davis, University of California, Berkeley

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, National Institute on Aging

Awards: Enhancing Reliable Data Sciences: Statistical Analysis for High-dimensional and Noisy Datasets with Complex Temporal and Nonlinear Structures, Simplified, Scalable 24-hour adaptive deep brain stimulation for Parkinson’s disease, The blood-brain barrier and Alzheimer pathology