Change Point Detection in the Frequency Domain with Statistical Reliability
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Abstract (arXiv)
Effective condition monitoring in complex systems requires identifying change points (CPs) in the frequency domain, as the structural changes often arise across multiple frequencies. This paper extends recent advancements in statistically significant CP detection, based on Selective Inference (SI), to the frequency domain. The proposed SI method quantifies the statistical significance of detected CPs in the frequency domain using p-values, ensuring that the detected changes reflect genuine structural shifts in the target system. We address two major technical challenges to achieve this. First, we extend the existing SI framework to the frequency domain by appropriately utilizing the properties of discrete Fourier transform (DFT). Second, we develop an SI method that provides valid p-values for CPs where changes occur across multiple frequencies. Experimental results demonstrate that the proposed method reliably identifies genuine CPs with strong statistical guarantees, enabling more accurate root-cause analysis in the frequency domain of complex systems.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 61 references that are in the map. First posted on arXiv 5 Feb 2025.
Builds on
28 of 61 references.
References · 28
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16 more
- 2018Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Datacited by 19
- 2015Selective Sequential Model Selectioncited by 17
- 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
- 2021Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuationcited by 16
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- 2021Tree-Values: selective inference for regression treescited by 15
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- 2017Selective Inference for Change Point Detection in Multi-dimensional Sequencescited by 11
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- 2020Selective Inference for Latent Block Modelscited by 3
- 2024Post-selection inference for quantifying uncertainty in changes in variancecited by 2
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