*Bounty: 200*

*Bounty: 200*

I have 3 time series which I can apply the wavelet transform to using a rolling window. The rolling window takes a single time series of length 200 and applies the waveslim::modwt function to it over the first 30 samples. This outputs 5 lists of which I am only interested in (d1,d2,d3,d4) and these each have a length of 30. A simple example can be found here:

```
library(waveslim)
J <- 4 #no. of levels in decomposition
data(ar1)
ar1.modwt <- modwt(ar1, "la8", J)
```

@G. Grothendieck has kindly provided a neat piece of code for the rolling window approach for a single time series here.

The rolling window increments by 1 and we go again, producing another 5 lists of which I only care for d1->d4 and so on and so on until the full length of the time series had been rolled over.

The next step is to apply the waveslim::brick.wall function to the output of the rolling window lists. The brick.wall function looks at the output of modwt for the first window across the 4 levels and replaces some of the values with NAs.

I believe I have covered this by modifying @G. Grothendieck answer using the following approach, I hope I am right:

```
modwt2 <- function(...) unlist(head(brick.wall(modwt(...)), 4))
rollr <- rollapplyr(ar1, 30, FUN = modwt2, wf = "la8", n.levels = 4, boundary = "periodic")
L <- lapply(1:nrow(rollr), function(i) matrix(rollr[i,], , 4))
```

The final piece is to construct correlation matrices for the outputs of the brick.wall function which is L above over the 4 levels of interest.

There is a function called waveslim::wave.correlation which takes two brick.wall outputs X and Y and computes the wave.correlation over the various levels.

```
library(waveslim)
data(exchange)
returns <- diff(log(as.matrix(exchange)))
returns <- ts(returns, start=1970, freq=12)
wf <- "la8"
J <- 4
demusd.modwt <- modwt(returns[,"DEM.USD"], wf, J)
demusd.modwt.bw <- brick.wall(demusd.modwt, wf)
jpyusd.modwt <- modwt(returns[,"JPY.USD"], wf, J)
jpyusd.modwt.bw <- brick.wall(jpyusd.modwt, wf)
returns.modwt.cor <- wave.correlation(demusd.modwt.bw, jpyusd.modwt.bw,
N = dim(returns)[1])
```

I wish to expand on this and compute the full correlation matrix for my 3 time series. Please note that the example above with exchange rates does not use the rolling window approach as it uses the full length of the time series which I would like to now do and it also produces a single values for the correlation between two time series. It does not construct the full correlation matrix which I need as I am interested in the eigenvalues of these correlation matrix over time.

I hope this makes sense because it has caused me a lot of heartache over the last few weeks and days.

So in summary:

- Take 3 time series
- Apply modwt function using rolling window
- Apply brick.wall function to each output of the rolling window in 2 above
- Create full 3×3 correlation matrix for the 4 levels using outputs of 3 above over time
Many thanks.