Structural classification of locally stationary time series based on second-order characteristics
Authors
Research Topics
Paper Information
-
Journal:
Journal of the Royal Statistical Society Series B -
DOI:
10.1093/jrsssb/qkag083 -
Published:
June 23, 2026 -
Added to Tracker:
Jun 24, 2026
Abstract
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.
Author Details
Xiucai Ding
AuthorLexin Li
AuthorChen Qian
AuthorResearch Topics & Keywords
Machine Learning
Research AreaTime Series
Research AreaCitation Information
APA Format
Xiucai Ding
,
Lexin Li
&
Chen Qian
(2026)
.
Structural classification of locally stationary time series based on second-order characteristics.
Journal of the Royal Statistical Society Series B
, 10.1093/jrsssb/qkag083.
BibTeX Format
@article{paper1306,
title = { Structural classification of locally stationary time series based on second-order characteristics },
author = {
Xiucai Ding
and Lexin Li
and Chen Qian
},
journal = { Journal of the Royal Statistical Society Series B },
year = { 2026 },
doi = { 10.1093/jrsssb/qkag083 },
url = { https://doi.org/10.1093/jrsssb/qkag083 }
}