# dwt

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##### Discrete Wavelet Transform

Computes the discrete wavelet transform coefficients for a univariate or multivariate time series.

Keywords
ts
##### Usage
dwt(X, filter="la8", n.levels, boundary="periodic", fast=TRUE)
##### Arguments
X

A univariate or multivariate time series. Numeric vectors, matrices and data frames are also accepted.

filter

Either a wt.filter object, a character string indicating which wavelet filter to use in the decomposition, or a numeric vector of wavelet coefficients (not scaling coefficients). See help(wt.filter) for acceptable filter names.

n.levels

An integer specifying the level of the decomposition. By default this is the value J such that the length of $X$ is at least as great as the length of the level $J$ wavelet filter, but less than the length of the level $J+1$ wavelet filter. Thus, $J \le \log{(\frac{N-1}{L-1}+1)}$, where $N$ is the length of $X$.

boundary

A character string indicating which boundary method to use. boundary = "periodic" and boundary = "reflection" are the only supported methods at this time.

fast

A logical flag which, if true, indicates that the pyramid algorithm is computed with an internal C function. Otherwise, only R code is used in all computations.

##### Details

The discrete wavelet transform is computed via the pyramid algorithm, using pseudocode written by Percival and Walden (2000), pp. 100-101. When boundary="periodic" the resulting wavelet and scaling coefficients are computed without making changes to the original series - the pyramid algorithm treats X as if it is circular. However, when boundary="reflection" a call is made to extend.series, resulting in a new series which is reflected to twice the length of the original series. The wavelet and scaling coefficients are then computed by using a periodic boundary condition on the reflected sereis, resulting in twice as many wavelet and scaling coefficients at each level.

##### Value

Returns an object of class dwt, which is an S4 object with slots

W

A list with element $i$ comprised of a matrix containing the $i$th level wavelet coefficients.

V

A list with element $i$ comprised of a matrix containing the $i$th level scaling coefficients.

filter

A wt.filter object containing information for the filter used in the decomposition. See help(wt.filter) for details.

level

An integer value representing the level of wavelet decomposition.

n.boundary

A numeric vector indicating the number of boundary coefficients at each level of the decomposition.

boundary

A character string indicating the boundary method used in the decomposition. Valid values are "periodic" or "reflection".

series

The original time series, X, in matrix format.

class.X

A character string indicating the class of the input series. Possible values are "ts", "mts", "numeric", "matrix", or "data.frame".

attr.X

A list containing the attributes information of the original time series, X. This is useful if X is an object of class ts or mts and it is desired to retain relevant time information. If the original time series, X, is a matrix or has no attributes, then attr.X is an empty list.

aligned

A logical value indicating whether the wavelet and scaling coefficients have been phase shifted so as to be aligned with relevant time information from the original series. The value of this slot is initially FALSE and can only be changed to TRUE via the align function, with the dwt object as input.

coe

A logical value indicating whether the center of energy method was used in phase alignement of the wavelet and scaling coefficients. By default, this value is FALSE (and will always be FALSE when aligned is FALSE) and will be set to true if the dwt object is phase shifted via the align function and center of energy method.

##### References

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

modwt, wt.filter.

• dwt
• print.dwt
• summary.dwt
##### Examples
# NOT RUN {
# obtain the two series listed in Percival and Walden (2000), page 42
X1 <- c(.2,-.4,-.6,-.5,-.8,-.4,-.9,0,-.2,.1,-.1,.1,.7,.9,0,.3)
X2 <- c(.2,-.4,-.6,-.5,-.8,-.4,-.9,0,-.2,.1,-.1,.1,-.7,.9,0,.3)

# combine them and compute DWT
newX <- cbind(X1,X2)
wt <- dwt(newX, n.levels=3, boundary="reflection", fast=FALSE)
# }

Documentation reproduced from package wavelets, version 0.3-0.1, License: GPL (>= 2)

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