qfa (version 4.1)

sqdft.fit: Spline Quantile Discrete Fourier Transform (SQDFT) of Time Series Given Smoothing Parameter

Description

This function computes spline quantile discrete Fourier transform (SQDFT) for univariate or multivariate time series through trigonometric spline quantile regression with user-supplied spar.

Usage

sqdft.fit(
  y,
  tau,
  spar = 1,
  d = 1,
  weighted = FALSE,
  ztol = 1e-05,
  n.cores = 1,
  cl = NULL
)

Value

A list with the following elements:

coefficients

matrix of regression coefficients

qdft

matrix or array of the spline quantile discrete Fouror BICier transform of y

crit

criteria for smoothing parameter selection: (AIC,BIC)

Arguments

y

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

tau

sequence of quantile levels in (0,1)

spar

smoothing parameter

d

subsampling rate of quantile levels (default = 1)

weighted

if TRUE, penalty function is weighted (default = FALSE)

ztol

zero tolerance parameter used to determine the effective dimensionality of the fit

n.cores

number of cores for parallel computing (default = 1)

cl

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

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.sqdft <- sqdft.fit(y,tau,spar=1,d=4)$qdft