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This approach detects structural change points in time series, indicating robustness across various applications.
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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Nini Xie (2026) studied this question.
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