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Wavelet-Packet-based Noise Signatures With Higher-Order Statistics for Anomaly Prediction

Signal Processing 2025-12-16 v1 Information Theory math.IT

Abstract

This note develops the first-ever noise-centric anomaly prediction method for a fused discrete-time signal. A Wavelet Packet Transform (WPT) provides a time--frequency expansion in which structure and residual can be separated via orthogonal projection. Higher-Order Statistics (HOS), particularly the third-order cumulant (and its bispectral interpretation), quantify non-Gaussianity and nonlinear coupling in the extracted residual. Compact noise signatures are constructed and an analytically calibrated Mahalanobis detector yields a closed-form decision rule with non-central chi-square performance under mean-shift alternatives. Propositions and proofs establish orthonormality, energy preservation, Gaussian-null behavior of cumulants, and the resulting test statistics.

Keywords

Cite

@article{arxiv.2512.12528,
  title  = {Wavelet-Packet-based Noise Signatures With Higher-Order Statistics for Anomaly Prediction},
  author = {Indrakshi Dey and Ilias Cherkaoui and Mohamed Khalafalla Hassan},
  journal= {arXiv preprint arXiv:2512.12528},
  year   = {2025}
}
R2 v1 2026-07-01T08:23:46.224Z