Wavelet Methods for Time Series Analysis

By Donald B. Percival, Andrew T. Walden. 2000

1,404 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/67befae7c41767e6a3a55418eb51394970bdb164

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In Analytical Chemistry

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Citations in the corpus

Testing the Suitability of Wavelet Preprocessing for TSK Fuzzy Models (2006, background): “It has been argued that wavelets are better suited for modeling time series that exhibit local behavior or structural changes [ 9 ].”
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study (2001, methodology): “Alternatively, the translation invariant thresholding is via the nondecimated discrete wavelet transform (NDWT) (or stationary or maximal overlap transform) – see, for example, Nason & Silverman (1995) or Percival & Walden (2000).”
Dynamic reconfiguration of human brain networks during learning (2010, methodology): “Accordingly, we used MODWT to decompose each regional time series into wavelet scales corresponding to specific frequency bands [19].”
ENHANCED MAGNETIC COMPRESSIBILITY AND ISOTROPIC SCALE INVARIANCE AT SUB-ION LARMOR SCALES IN SOLAR WIND TURBULENCE (2010, methodology): “Details of the actual fast pyramidal algorithms used to implement the UDWT can be found in (Percival & Walden 2000) and (Mallat 2009).”
Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis (2020, background): “Wavelet analysis is a mathematical tool that can reveal information within the signals in both the time and scale (frequency) domains [27] .”
Estimators of Long-Memory: Fourier versus Wavelets (2008, methodology): “present a rich time/frequency representation of the process, which can be more informative than that of the classical Fourier analysis, as discussed in Serroukh et al. (2000), Stoev et al. (2006) and Percival and Walden (2006). Wavelets can be used to detect the presence of outliers or jumps in the ”
Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models (2004, methodology): “…the Daubechies least asymmetric wavelet with four vanishing moments because it favourably addresses each of the above points while possessing the extraordinary theoretical property of diagonalizing a longmemory process’ covariance matrix (Whitcher, 1998; Percival and Walden, 2000, pp. 346–349).”
Rectification of the Bias in the Wavelet Power Spectrum (2007, background): “See Daubechies (1992), Chui (1992), Meyer (1992), Strang and Nguyen (1997), Percival and Walden (2000), and references therein, for a historical account.”