fastWavelets (version 1.0.1)

mo_dwt: Maximal Overlap Discrete Wavelet Transform (MODWT)

Description

This function calculates the wavelet and scaling coefficients of the MODWT.

Usage

mo_dwt(X, wavelet, decomp_level)

Value

Wavelet and scaling coefficients (N x J+1) (wavelet coefficients in first J columns of returned matrix)

Arguments

X

An (N x 1) matrix or a vector

wavelet

Scaling filter name (see Details below) (string)

decomp_level

Decomposition level (integer, 1 < decomp_level < N/2)

Details

The argument wavelet can take one of the following values:

c("haar", "d1", "sym1", "d2", "sym2", "d3", "sym3", "d4", "d5", "d6", "d7", "d8", "d9", "d10", "d11", "sym4", "sym5", "sym6", "sym7", "sym8", "sym9", "sym10", "coif1", "coif2", "coif3", "coif4", "coif5", "la8", "la10", "la12", "la14", "la16", "la18", "la20", "bl14", "bl18", "bl20", "fk4", "fk6", "fk8", "fk14", "fk18", "fk22", "mb4.2", "mb8.2", "mb8.3", "mb8.4", "mb10.3", "mb12.3", "mb14.3", "mb16.3", "mb18.3", "mb24.3", "mb32.3", "beyl", "vaid", "han2.3", "han3.3", "han4.5", "han5.5" )

References

M. Basta (2014), Additive Decomposition and Boundary Conditions in Wavelet-Based Forecasting Approaches, Acta Oeconomica Pragensia, 2, pp. 48-70.

Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.

Examples

N <- 1000 #  number of time series points
J <- 4 # decomposition level
wavelet <- 'coif1' # wavelet filter
X <- matrix(rnorm(N),N,1)
W <- mo_dwt(X,wavelet,J)