sqdft.fit: Spline Quantile Discrete Fourier Transform (SQDFT) of Time...

In qfa: Quantile-Frequency Analysis (QFA) of Time Series

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
)

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)

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)

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

qfa documentation built on April 11, 2025, 5:49 p.m.