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April 10, 2026Open Access

A self-normalization and support vector regression based approach for detecting structural change points in time series

Key Points

The study aims to develop a method for detecting structural change points in time series without the need for parameter tuning.
Developed a self-normalization framework
Utilized support vector regression for analysis
Tested across various time series with nonlinear characteristics
Analyzed structural instability without relying on tuning parameters
Demonstrated robustness in detecting change points
Showed consistent performance across different datasets
Confirmed applicability in both scientific and practical contexts

Abstract

The proposed framework provides a robust, parameter-free tool for analyzing structural instability in time series, with particular advantages in handling complex, nonlinear data structures. The method's avoidance of tuning parameters and consistent performance across different domains suggest broad applicability in scientific research and practical applications.

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Authors

Nini Xie

Journals

PLoS ONE

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Institutions

Qinghai Normal University

Xi'an University of Finance and Economics

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A self-normalization and support vector regression based approach for detecting structural change points in time series

Key Points

Abstract

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Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

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