Process / pipelineTranslation-invariant wavelet decomposition

MODWT

The maximal overlap discrete wavelet transform (MODWT) is a translation-invariant wavelet decomposition method that addresses a key limitation of the standard DWT: lack of shift invariance. Introduced by Percival and Walden (1995), MODWT applies the same wavelet filters at each scale without downsampling, producing an undecimated decomposition. Each detail and approximation coefficient array maintains the full length of the input signal, enabling both robust multi-scale analysis and translation-invariant feature extraction.

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Sources

  1. Percival, D. B., & Walden, A. T. (1995). Wavelet Methods for Time Series Analysis. Cambridge University Press. DOI: 10.1017/CBO9780511841263
  2. Percival, D. B. (2000). Wavelet methods for time series analysis. Cambridge University Press. link
  3. Whitcher, B., Guttorp, P., & Percival, D. B. (2000). Wavelet analysis of covariance with application to atmospheric time series. Journal of Geophysical Research, 105(D11), 14941–14962. DOI: 10.1029/2000JD900110

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Referenced by

ScholarGateMODWT (Maximal Overlap Discrete Wavelet Transform). Retrieved 2026-06-04 from https://scholargate.app/en/time-series/modwt