# 1D Autocorrelation Wavelets Transform

The 1D AC Wavelet transform will take a 1D signal such as a single time series and decompose it into wavelet coefficients.

## Orthogonal Filters

To begin, one must first specify the high pass and low pass autocorrelation filters to use in the decomposition. The types of filters available for use are `Haar`

, `Coiflet`

, `Daubechies`

, `Symlet`

, `Battle`

, `Beylkin`

, `Vaidyanathan`

, and `CDF`

. These filters are implemented within the Wavelets.jl package.

## Forward AC Wavelet Transform

To perform the transform on a signal, use the `acwt`

function.

`AutocorrelationShell.ACTransforms.acwt`

— Function`acwt(x, wt[, L=maxtransformlevels(x)])`

Perform a forward ac wavelet transform of the array `x`

. This method works for the 2D case as well. The wavelet type `wt`

determines the transform type. Refer to Wavelet.jl for a list of available methods.

**Examples**

`acwt(x, wavelet(WT.db4))`

**See also:**`iacwt`

## Inverse AC Wavelet Transform

To reconstruct the signal from a array of AC Wavelet coefficients, use the `iacwt`

function.

`AutocorrelationShell.ACTransforms.iacwt`

— Function`iacwt`

The inverse of `acwt`

**See also:**`acwt`