qdft {qfa}

Quantile Discrete Fourier Transform (QDFT)

Description

This function computes quantile discrete Fourier transform (QDFT) for univariate or multivariate time series.

Usage

qdft(y, tau, n.cores = 1, cl = NULL)

Arguments

y

vector or matrix of time series (if matrix, nrow(y) = length of time series)

tau

sequence of quantile levels in (0,1)

n.cores

number of cores for parallel computing (default = 1)

cl

pre-existing cluster for repeated parallel computing (default = NULL)

Value

matrix or array of quantile discrete Fourier transform of y

Examples

y <- stats::arima.sim(list(order=c(1,0,0), ar=0.5), n=64)
tau <- seq(0.1,0.9,0.05)
y.qdft <- qdft(y,tau)
# Make a cluster for repeated use
n.cores <- 2
cl <- parallel::makeCluster(n.cores)
parallel::clusterExport(cl, c("tqr.fit"))
doParallel::registerDoParallel(cl)
y1 <- stats::arima.sim(list(order=c(1,0,0), ar=0.5), n=64)
y.qdft <- qdft(y1,tau,n.cores=n.cores,cl=cl)
y2 <- stats::arima.sim(list(order=c(1,0,0), ar=0.5), n=64)
y.qdft <- qdft(y2,tau,n.cores=n.cores,cl=cl)
parallel::stopCluster(cl)