Wavelet Methods for Time Series Analysis

By Todd Ogden. 2002

690 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/aa7f302802603f377b2e74eec8ead162706cb645

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Citations in the corpus

Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh (2017, methodology): “Discrete wavelet transformation is the basic tool required for analyzing time series via wavelets and plays a role analogous to that of the discrete Fourier transformation in spectral analysis (Percival and Walden, 2000).”
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series (2014, methodology): “This was done using the inverse pyramid algorithm (Percival and Walden, 2006):”
Oil price and exchange rates: A wavelet based analysis for India (2013, background): “However, the choice of a particular wavelet filter is not so crucial (Percival and Walden, 2000).”
Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks (2014, methodology): “In financial time series modeling and forecasting, various types of wavelets can be used, such as the Haar, Mexican Hat, Morlet and Daubechies wavelets (Rao and Bopardikar, 1998; Percival and Walden, 2000).”
Constructing a quasilinear moving average using the scaling function (2009, background): “The definition below is mainly based on Percival & Walden (2000), Mallat (2003) and Farge (1992).”
AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network (2012, methodology): “Multiresolution analysis of wind series is carried out using Least Asymmetry-8 (LA-8) wavelet and scaling filter [27].”
Estimating Granger causality from fourier and wavelet transforms of time series data. (2007, methodology): “On the other hand, the Fourier and wavelet transform-based nonparametric spectral methods are known to be free from such difficulties [13] and have been used extensively in the analysis of univariate and multivariate experimental time series [14, 15].”
Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data (2011, methodology): “This variance varies approximately as ταj for large τj [Percival and Walden, 2000, equation (297b)], where α is the fractal index, from which the fractal dimension can be deduced; see Table 2.”
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.”