Package 'longmemo'

Title:	Statistics for Long-Memory Processes (Book Jan Beran), and Related Functionality
Description:	Datasets and Functionality from 'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall. Estimation of Hurst (and more) parameters for fractional Gaussian noise, 'fARIMA' and 'FEXP' models.
Authors:	Jan Beran [aut] (original S functions and scripts), Martin Maechler [cre, aut] (Toplevel R functions and much more, <https://orcid.org/0000-0002-8685-9910>), Brandon Whitcher [ctb] (Datasets)
Maintainer:	Martin Maechler <[email protected]>
License:	GPL (>= 2)
Version:	1.1-3
Built:	2024-08-30 03:35:57 UTC
Source:	https://github.com/cran/longmemo

Help Index

Covariance for fractional ARIMA
Covariance Matrix of Eta for Fractional Gaussian Noise
Covariances of a Fractional ARIMA(0,d,0) Process
Covariances of a Fractional Gaussian Process
Ethernet Traffic Data Set
Fractional EXP (FEXP) Model Estimator
Log-Log and Log-X Plot of Spectrum
NBS measurement deviations from 1 kg
Northern Hemisphere Temperature
Nile River Minima, yearly 622–1284
Simple Periodogram Estimate
Plot Method for FEXP and WhittleEst Model Fits
Approximate Log Likelihood for Fractional Gaussian Noise / Fractional ARIMA
Simulate (Fractional) Gaussian Processes
Spectral Density of Fractional ARMA Process
Spectral Density of Fractional Gaussian Noise
Video VBR data
Whittle Estimator for Fractional Gaussian Noise / Fractional ARIMA

Covariance for fractional ARIMA

Description

Compute the covariance matrix of $\hat{eta}$ for a fractional ARIMA process.

Usage

CetaARIMA(eta, p, q, m = 10000, delta = 1e-9)
CetaARIMA(eta, p, q, m = 10000, delta = 1e-9)

Arguments

`eta`	parameter vector `eta = c(H, phi, psi)`.
`p`, `q`	integer scalars giving the AR and MA order respectively.
`m`	integer specifying the length of the Riemann sum, with step size `2 * pi/m`.
`delta`	step size for numerical derivative computation.

Details

builds on calling specARIMA(eta,p,q,m)

Value

the (square) matrix containg covariances up to ...

Author(s)

Jan Beran (principal) and Martin Maechler (fine tuning)

References

Beran(1984), listing on p.224–225.

Examples

 (C.7  <- CetaARIMA(0.7, m = 256, p = 0, q = 0))
 (C.5  <- CetaARIMA(eta = c(H = 0.5, phi=c(-.06, 0.42, -0.36), psi=0.776),
                    m = 256, p = 3, q = 1))
(C.7  <- CetaARIMA(0.7, m = 256, p = 0, q = 0))
 (C.5  <- CetaARIMA(eta = c(H = 0.5, phi=c(-.06, 0.42, -0.36), psi=0.776),
                    m = 256, p = 3, q = 1))

Covariance Matrix of Eta for Fractional Gaussian Noise

Description

Covariance matrix of $\hat{\eta}$ for fractional Gaussian noise (fGn).

Usage

CetaFGN(eta, m = 10000, delta = 1e-9)
CetaFGN(eta, m = 10000, delta = 1e-9)

Arguments

`eta`	parameter vector `eta = c(H, *)`.
`m`	integer specifying the length of the Riemann sum, with step size `2 * pi/m`. The default (10000) is realistic.
`delta`	step size for numerical derivative computation.

Details

Currently, the step size for numerical derivative is the same in all coordinate directions of eta. In principle, this can be far from optimal.

Value

Variance-covariance matrix of the estimated parameter vector $\hat{\eta}$ .

Author(s)

Jan Beran (principal) and Martin Maechler (speedup, fine tuning)

Examples

 (C.7  <- CetaFGN(0.7, m = 256))
 (C.5  <- CetaFGN(eta = c(H = 0.5), m = 256))
 (C.5. <- CetaFGN(eta = c(H = 0.5), m = 1024))
(C.7  <- CetaFGN(0.7, m = 256))
 (C.5  <- CetaFGN(eta = c(H = 0.5), m = 256))
 (C.5. <- CetaFGN(eta = c(H = 0.5), m = 1024))

Covariances of a Fractional ARIMA(0,d,0) Process

Description

Compute the Autocovariances of a fractional ARIMA(0,d,0) process (d = H - 1/2).

Usage

ckARMA0(n, H)
ckARMA0(n, H)

Arguments

`n`	sample size (length of time series).
`H`	self-similarity (‘Hurst’) parameter.

Details

The theoretical formula,

$C(k) = (-1)^k \Gamma(1-2d) / (\Gamma(k+1-d) \Gamma(1-k-d)) ,$

where $d = H - 1/2$ , leads to over-/underflow for larger lags $k$ ; hence use the asymptotical formula there.

Value

numeric vector of length n of covariances $C(0) \ldots C(n-1)$ .

Author(s)

Jan Beran (principal) and Martin Maechler (speedup, fine tuning)

References

Jan Beran (1994), p.63, (2.35) and (2.39).

Examples

str(C.8 <- ckARMA0(50, H = 0.8))
yl <- c(0,max(C.8))
plot(0:49, C.8, type = "h", ylim = yl)
plot(0:49, C.8, type = "h", log = "xy",
     main = "Log-Log  ACF for ARIMA(0,d,0)")
str(C.8 <- ckARMA0(50, H = 0.8))
yl <- c(0,max(C.8))
plot(0:49, C.8, type = "h", ylim = yl)
plot(0:49, C.8, type = "h", log = "xy",
     main = "Log-Log  ACF for ARIMA(0,d,0)")

Covariances of a Fractional Gaussian Process

Description

Compute the Autocovariances of a fractional Gaussian process

Usage

ckFGN0(n, H)
ckFGN0(n, H)

Arguments

`n`	sample size (length of time series).
`H`	self-similarity (‘Hurst’) parameter.

Value

numeric vector of covariances upto lag n-1.

Author(s)

Jan Beran (principal) and Martin Maechler (fine tuning)

Examples

str(C.8 <- ckFGN0(50, H = 0.8))
plot(0:49, C.8, type = "h", ylim = 0:1)
plot(0:49, C.8, type = "h", log = "xy",
     main = "Log-Log  ACF for frac.GaussNoise(H = 0.8)")
str(C.8 <- ckFGN0(50, H = 0.8))
plot(0:49, C.8, type = "h", ylim = 0:1)
plot(0:49, C.8, type = "h", log = "xy",
     main = "Log-Log  ACF for frac.GaussNoise(H = 0.8)")

Ethernet Traffic Data Set

Description

Ethernet traffic data from a LAN at Bellcore, Morristown (Leland et al. 1993, Leland and Wilson 1991). The data are listed in chronological sequence by row.

Usage

data(ethernetTraffic)data(ethernetTraffic)

Format

A times series of length 4000.

Source

Jan Beran and Brandon Whitcher by E-mail in fall 1995.

Examples

data(ethernetTraffic)
str(ethernetTraffic)
plot(ethernetTraffic)## definitely special
data(ethernetTraffic)
str(ethernetTraffic)
plot(ethernetTraffic)## definitely special

Fractional EXP (FEXP) Model Estimator

Description

FEXPest(x, *) computes Beran's Fractional EXP or ‘FEXP’ model estimator.

.ffreq(n) returns the Fourier frequencies $\frac{2\pi j}{n}$ (of a time series of length n).

Usage

FEXPest(x, order.poly, pvalmax, verbose = FALSE)
## S3 method for class 'FEXP'
print(x, digits = getOption("digits"), ...)

.ffreq(n, full = FALSE)
FEXPest(x, order.poly, pvalmax, verbose = FALSE)
## S3 method for class 'FEXP'
print(x, digits = getOption("digits"), ...)

.ffreq(n, full = FALSE)

Arguments

`x`	numeric vector representing a time series.
`order.poly`	integer specifying the maximal polynomial order that should be taken into account. `order.poly = 0` is equivalent to a FARIMA(0,d,0) model.
`pvalmax`	maximal P-value – the other iteration stopping criterion and “model selection tuning parameter”. Setting this to `1`, will use `order.poly` alone, and hence the final model order will be `= order.poly`.
`verbose`	logical indicating if iteration output should be printed.
`digits`, `...`	optional arguments for `print` method, see `print.default`.
`n`	a positive integer, typically the length of a time series.
`full`	logical indicating if `n %/% 2` or by default “only” `(n-1) %/% 2` values should be returned.

Value

FEXPest(x,..) returns an object of class FEXP, basically a list with components

`call`	the function `call`.
`n`	time series length `length(x)`.
`H`	the “Hurst” parameter which is simply `(1-theta[2])/2`.
`coefficients`	numeric 4-column matrix as returned from `summary.glm()`, with estimate of the full parameter vector $\theta$ , its standard error estimates, t- and P-values, as from the `glm(*, family = Gamma)` fit.
`order.poly`	the effective polynomial order used.
`max.order.poly`	the original `order.poly` (argument).
`early.stop`	logical indicating if `order.poly` is less than `max.order.poly`, i.e., the highest order polynomial terms were dropped because of a non-significant P-value.
`spec`	the spectral estimate $f(\omega_j)$ , at the Fourier frequencies $\omega_j$ . Note that `.ffreq(x$n)` recomputes the Fourier frequencies vector (from a fitted FEXP or WhittleEst model `x`).
`yper`	raw periodogram of (centered and scaled `x`) at Fourier frequencies $I(\omega_j)$ .

There currently are methods for print(), plot and lines (see plot.FEXP) for objects of class "FEXP".

Author(s)

Martin Maechler, using Beran's “main program” in Beran(1994), p.234 ff

References

Beran, Jan (1993) Fitting long-memory models by generalized linear regression. Biometrika 80, 817–822.

Beran, Jan (1994). Statistics for Long-Memory Processes; Chapman & Hall.

Examples

data(videoVBR)
(fE  <- FEXPest(videoVBR, order = 3, pvalmax = .5))
(fE3 <- FEXPest(videoVBR, order = 3, pvalmax = 1 ))

(fE7 <- FEXPest(videoVBR, order = 3, pvalmax = 0.10))
##--> this also choses order 2, as "FE" :
all.equal(fE $coef,
          fE7$coef) # -> TRUE

confint(fE)
confint(fE7, level = 0.99)

.ffreq(8)
.ffreq(8, TRUE)
stopifnot(all.equal((1:3)/4,
                    .ffreq(8) / pi))
data(videoVBR)
(fE  <- FEXPest(videoVBR, order = 3, pvalmax = .5))
(fE3 <- FEXPest(videoVBR, order = 3, pvalmax = 1 ))

(fE7 <- FEXPest(videoVBR, order = 3, pvalmax = 0.10))
##--> this also choses order 2, as "FE" :
all.equal(fE $coef,
          fE7$coef) # -> TRUE

confint(fE)
confint(fE7, level = 0.99)

.ffreq(8)
.ffreq(8, TRUE)
stopifnot(all.equal((1:3)/4,
                    .ffreq(8) / pi))

Log-Log and Log-X Plot of Spectrum

Description

Log-Log and “Log-X” plot of spectrum. Very simple utilities, kept here mainly for back compatibility, as they appear in the book scripts.

Usage

llplot(yper, spec)
lxplot(yper, spec)
llplot(yper, spec)
lxplot(yper, spec)

Arguments

`yper`	periodogram values
`spec`	spectrum values

Author(s)

Jan Beran (principal) and Martin Maechler (speedup, fine tuning)

NBS measurement deviations from 1 kg

Description

NBS weight measurements - deviation from 1 kg in micrograms, see the references. The data are listed in chronological sequence by row.

Usage

data(NBSdiff1kg)data(NBSdiff1kg)

Format

A time series of length 289.

Source

Jan Beran and Brandon Whitcher by E-mail in fall 1995.

References

H.P. Graf, F.R. Hampel, and J.Tacier (1984). The problem of unsuspected serial correlations. In J. Franke, W. Härdle, and R.D. Martin, editors, Robust and Nonlinear Time Series Analysis, Lecture Notes in Statistics 26, 127–145; Springer.

Pollak, M., Croakin, C., and Hagwood, C. (1993). Surveillance schemes with applications to mass calibration. NIST report 5158; Gaithersburg, MD.

Examples

data(NBSdiff1kg)
plot(NBSdiff1kg)
data(NBSdiff1kg)
plot(NBSdiff1kg)

Northern Hemisphere Temperature

Description

Monthly temperature for the northern hemisphere for the years 1854-1989, from the data base held at the Climate Research Unit of the University of East Anglia, Norwich, England. The numbers consist of the temperature (degrees C) difference from the monthly average over the period 1950-1979.

Usage

data(NhemiTemp)data(NhemiTemp)

Format

Time-Series (ts) of length 1632, frequency 12, starting 1854, ending 1990.

Source

Jan Beran and Brandon Whitcher by E-mail in fall 1995.

References

Jones, P.D. and Briffa, K.R. (1992) Global surface air temperature variations during the twentieth century, part 1. The Holocene 2, 165–179.

Jan Beran (1994). Dataset no. 5, p.29–31.

Examples

data(NhemiTemp)
plot(NhemiTemp)
mean(window(NhemiTemp, 1950,1979))# (about) 0 ``by definition''
data(NhemiTemp)
plot(NhemiTemp)
mean(window(NhemiTemp, 1950,1979))# (about) 0 ``by definition''

Nile River Minima, yearly 622–1284

Description

Yearly minimal water levels of the Nile river for the years 622 to 1281, measured at the Roda gauge near Cairo, (Tousson, p. 366–385).

Usage

data(NileMin)data(NileMin)

Format

Time-Series (ts) of length 663.

Source

The original Nile river data supplied by Beran only contained only 500 observations (622 to 1121). However, the book claimed to have 660 observations (622 to 1281). First added the remaining observations from the book by hand, and still came up short with only 653 observations (622 to 1264). Finally have 663 observations : years 622–1284 (as in orig. source)

References

Tousson, O. (1925). Mémoire sur l'Histoire du Nil; Mémoire de l'Institut d'Egypte.

Jan Beran (1994). Dataset no.1, p.20–22.

Examples

data(NileMin)
plot(NileMin, main = "Nile River Minima 622 - 1284")
data(NileMin)
plot(NileMin, main = "Nile River Minima 622 - 1284")

Simple Periodogram Estimate

Description

Simply estimate the periodogram via the Fast Fourier Transform.

Usage

per(z)
per(z)

Arguments

`z`	numeric vector with the series to compute the periodogram from.

Details

This is basically the same as
spec.pgram(z, fast = FALSE, detrend = FALSE, taper = 0) $ spec, and not really recommended to use — exactly for the reason that spec.pgram has the defaults differently, fast = TRUE, detrend = TRUE, taper = 0.1, see that help page.

Value

numeric vector of length $1 + floor(n/2)$ where n = length(z).

Author(s)

Jan Beran (principal) and Martin Maechler (fine tuning)

Examples

 data(NileMin)
 plot(10*log10(per(NileMin)), type='l')
data(NileMin)
 plot(10*log10(per(NileMin)), type='l')

Plot Method for FEXP and WhittleEst Model Fits

Description

(S3) methods for the generic functions plot (and lines) applied to fractional EXP (FEXP) and "WhittleEst" (FEXPest, models. plot() plots the data periodogram and the ‘FEXP'’ model estimated spectrum, where lines() and does the latter.

Usage

## S3 method for class 'FEXP'
plot(x, log = "xy", type = "l",
      col.spec = 4, lwd.spec = 2, xlab = NULL, ylab = expression(hat(f)(nu)),
      main = paste(deparse(x$call)[1]), sub = NULL, ...)

## (With identical argument list:)
## S3 method for class 'WhittleEst'
plot(x, log = "xy", type = "l",
      col.spec = 4, lwd.spec = 2, xlab = NULL, ylab = expression(hat(f)(nu)),
      main = paste(deparse(x$call)[1]), sub = NULL, ...)

## S3 method for class 'FEXP'
      lines(x, type = "l", col = 4, lwd = 2, ...)
## S3 method for class 'WhittleEst'
lines(x, type = "l", col = 4, lwd = 2, ...)
## S3 method for class 'FEXP'
plot(x, log = "xy", type = "l",
      col.spec = 4, lwd.spec = 2, xlab = NULL, ylab = expression(hat(f)(nu)),
      main = paste(deparse(x$call)[1]), sub = NULL, ...)

## (With identical argument list:)
## S3 method for class 'WhittleEst'
plot(x, log = "xy", type = "l",
      col.spec = 4, lwd.spec = 2, xlab = NULL, ylab = expression(hat(f)(nu)),
      main = paste(deparse(x$call)[1]), sub = NULL, ...)

## S3 method for class 'FEXP'
      lines(x, type = "l", col = 4, lwd = 2, ...)
## S3 method for class 'WhittleEst'
lines(x, type = "l", col = 4, lwd = 2, ...)

Arguments

`x`	an R object of `class` `"FEXP"`, as from `FEXPest()`.
`log`	character specifying log scale should be used, see `plot.default`. Note that the default log-log scale is particularly sensible for long-range dependence.
`type`	plot type for the periodogram, see `plot.default`.
`col.spec`, `lwd.spec`, `col`, `lwd`	graphical parameters used for drawing the estimated spectrum, see `lines`.
`xlab`, `ylab`, `main`, `sub`	labels for annotating the plot, see `title`, each with a sensible default.
`...`	further arguments passed to `plot.default`.

Author(s)

Martin Maechler

Examples

data(videoVBR)
fE <- FEXPest(videoVBR, order = 3, pvalmax = .5)
plot(fE)
fE3 <- FEXPest(videoVBR, order = 3, pvalmax = 1)#-> order 3
lines(fE3, col = "red3", lty=2)

f.GN    <- WhittleEst(videoVBR)
f.am21  <- WhittleEst(videoVBR, model = "fARIMA",
                      start= list(H= .5, AR = c(.5,0), MA= .5))
lines(f.GN,   col = "blue4")
lines(f.am21, col = "goldenrod")

##--- Using a tapered periodogram ---------
spvVBR <- spec.pgram(videoVBR, fast=FALSE, plot=FALSE)
fam21 <- WhittleEst(periodogr.x = head(spvVBR$spec, -1),
                    n = length(videoVBR), model = "fARIMA",
                    start= list(H= .5, AR = c(.5,0), MA= .5))
fam21
f.am21 # similar but slightly different

plot(fam21)

## Now, comparing to traditional ("log-X", not "log-log") spectral plot:
plot(fam21, log="y")

## compared to the standard R spectral plot : s
if(dev.interactive(TRUE)) getOption("device")()# a new graphics window
plot(spvVBR, log = "yes", col="gray50")
all.equal(.ffreq(fE$n) / (2*pi) -> ffr.,
          head(spvVBR$freq, -1))# TRUE
lines(ffr., fam21$spec, col=4, lwd=2)
## need to adjust for different 'theta1':
lines(ffr., f.am21$spec * fam21$theta1 / f.am21$theta1,
      col = adjustcolor("tomato", 0.6), lwd=2)
data(videoVBR)
fE <- FEXPest(videoVBR, order = 3, pvalmax = .5)
plot(fE)
fE3 <- FEXPest(videoVBR, order = 3, pvalmax = 1)#-> order 3
lines(fE3, col = "red3", lty=2)

f.GN    <- WhittleEst(videoVBR)
f.am21  <- WhittleEst(videoVBR, model = "fARIMA",
                      start= list(H= .5, AR = c(.5,0), MA= .5))
lines(f.GN,   col = "blue4")
lines(f.am21, col = "goldenrod")

##--- Using a tapered periodogram ---------
spvVBR <- spec.pgram(videoVBR, fast=FALSE, plot=FALSE)
fam21 <- WhittleEst(periodogr.x = head(spvVBR$spec, -1),
                    n = length(videoVBR), model = "fARIMA",
                    start= list(H= .5, AR = c(.5,0), MA= .5))
fam21
f.am21 # similar but slightly different

plot(fam21)

## Now, comparing to traditional ("log-X", not "log-log") spectral plot:
plot(fam21, log="y")

## compared to the standard R spectral plot : s
if(dev.interactive(TRUE)) getOption("device")()# a new graphics window
plot(spvVBR, log = "yes", col="gray50")
all.equal(.ffreq(fE$n) / (2*pi) -> ffr.,
          head(spvVBR$freq, -1))# TRUE
lines(ffr., fam21$spec, col=4, lwd=2)
## need to adjust for different 'theta1':
lines(ffr., f.am21$spec * fam21$theta1 / f.am21$theta1,
      col = adjustcolor("tomato", 0.6), lwd=2)

Approximate Log Likelihood for Fractional Gaussian Noise / Fractional ARIMA

Description

Qeta() ( $= \tilde{Q}(\eta)$ of Beran(1994), p.117) is up to scaling the negative log likelihood function of the specified model, i.e., fractional Gaussian noise or fractional ARIMA.

Usage

Qeta(eta, model = c("fGn","fARIMA"), n, yper, pq.ARIMA, give.B.only = FALSE)
Qeta(eta, model = c("fGn","fARIMA"), n, yper, pq.ARIMA, give.B.only = FALSE)

Arguments

`eta`	parameter vector = (H, phi[1:p], psi[1:q]).
`model`	character specifying the kind model class.
`n`	data length
`yper`	numeric vector of length `(n-1)%/% 2`, the periodogram of the (scaled) data, see `per`.
`pq.ARIMA`	integer, = c(p,q) specifying models orders of AR and MA parts — only used when `model = "fARIMA"`.
`give.B.only`	logical, indicating if only the `B` component (of the `Values` list below) should be returned. Is set to `TRUE` for the Whittle estimator minimization.

Details

Calculation of $A, B$ and $T_n = A/B^2$ where $A = 2\pi/n \sum_j 2*[I(\lambda_j)/f(\lambda_j)]$ , $B = 2\pi/n \sum_j 2*[I(\lambda_j)/f(\lambda_j)]^2$ and the sum is taken over all Fourier frequencies $\lambda_j = 2\pi*j/n$ , ( $j=1,\dots,(n-1)/2$ ).

$f$ is the spectral density of fractional Gaussian noise or fractional ARIMA(p,d,q) with self-similarity parameter $H$ (and $p$ AR and $q$ MA parameters in the latter case), and is computed either by specFGN or specARIMA.

$cov(X(t),X(t+k)) = \int \exp(iuk) f(u) du$

Value

a list with components

`n`	= input
`H`	(input) Hurst parameter, = `eta[1]`.
`eta`	= input
`A`, `B`	defined as above.
`Tn`	the goodness of fit test statistic $Tn= A/B^2$ defined in Beran (1992)
`z`	the standardized test statistic
`pval`	the corresponding p-value P(W > z)
`theta1`	the scale parameter $\hat{\theta_1} = \frac{{\hat\sigma_\epsilon}^2}{2\pi}$ such that $f()= \theta_1 f_1()$ and $integral(\log[f_1(.)]) = 0$ .
`spec`	scaled spectral density $f_1$ at the Fourier frequencies $\omega_j$ , see `FEXPest`; a numeric vector.

Note

yper[1] must be the periodogram $I(\lambda_1)$ at the frequency $2\pi/n$ , i.e., not the frequency zero !

Author(s)

Jan Beran (principal) and Martin Maechler (fine tuning)

References

Jan Beran (1992). A Goodness-of-Fit Test for Time Series with Long Range Dependence. JRSS B 54, 749–760.

Beran, Jan (1994). Statistics for Long-Memory Processes; Chapman & Hall. (Section 6.1, p.116–119; 12.1.3, p.223 ff)

Examples

data(NileMin)
y <- NileMin
n <- length(y)
yper <- per(scale(y))[2:(1+ (n-1) %/% 2)]
eta <- c(H = 0.3)
q.res <- Qeta(eta, n=n, yper=yper)
str(q.res)
data(NileMin)
y <- NileMin
n <- length(y)
yper <- per(scale(y))[2:(1+ (n-1) %/% 2)]
eta <- c(H = 0.3)
q.res <- Qeta(eta, n=n, yper=yper)
str(q.res)

Simulate (Fractional) Gaussian Processes

Description

Simulation of a Gaussian series $X(1), \dots, X(n)$ . Whereas simGauss works from autocovariances, where simFGN0 and simARMA0 call it, for simulating a fractional ARIMA(0,d,0) process ( $d = H-1/2$ ), or fractional Gaussian noise, respectively.

Usage

simARMA0  (n, H)
simFGN0   (n, H)
simFGN.fft(n, H, ...)
simGauss(autocov)
simARMA0  (n, H)
simFGN0   (n, H)
simFGN.fft(n, H, ...)
simGauss(autocov)

Arguments

`n`	length of time series
`H`	self-similarity parameter
`...`	optional arguments passed to `B.specFGN()`.
`autocov`	numeric vector of auto covariances $\gamma(0), \ldots, \gamma(n-1)$ .

Details

simGauss implements the method by Davies and Harte which is relatively fast using the FFT (fft) twice.

To simulate ARIMA(p, d, q), (for d in (-1/2, 1,2), you can use arima.sim(n, model = list(ar= .., ma = ..), innov= simARMA0(n,H=d+1/2) , n.start = 0).

simFGN.fft() is about twice as fast as simFGN0() and uses Paxson's proposal, by default via B.specFGN(*, k.approx=3, adjust=TRUE).

Value

The simulated series $X(1), \dots, X(n)$ , an R object of class "ts", constructed from ts().

Author(s)

Jan Beran (original) and Martin Maechler (simGauss, speedup, simplication). simFGN.fft: Vern Paxson.

References

Beran (1994), 11.3.3, p.216 f, referring to

Davis, R.B. and Harte, D.S. (1987). Tests for Hurst effect, Biometrika 74, 95–102.

Vern Paxson (1997). Fast, Approximate Synthesis of Fractional Gaussian Noise for Generating Self-Similar Network Traffic; Computer Communications Review 27 5, 5–18.

Examples

  x1 <- simFGN0(100, 0.7)
  x2 <- simARMA0(100, 0.7)
  plot(simFGN0(1000, 0.8)) #- time series plot
x1 <- simFGN0(100, 0.7)
  x2 <- simARMA0(100, 0.7)
  plot(simFGN0(1000, 0.8)) #- time series plot

Spectral Density of Fractional ARMA Process

Description

Calculate the spectral density of a fractional ARMA process with standard normal innovations and self-similarity parameter H.

Usage

specARIMA(eta, p, q, m)
specARIMA(eta, p, q, m)

Arguments

`eta`	parameter vector `eta = c(H, phi, psi)`.
`p`, `q`	integers giving AR and MA order respectively.
`m`	sample size determining Fourier frequencies.

Details

at the Fourier frequencies $2*\pi*j/n$ , ( $j=1,\dots,(n-1)$ ), cov(X(t),X(t+k)) = (sigma/(2*pi))*integral(exp(iuk)g(u)du).

— or rather – FIXME –

1. cov(X(t),X(t+k)) = integral[ exp(iuk)f(u)du ]

2. f() = theta1 * f*() ; spec = f*(), and integral[log(f*())] = 0

Value

an object of class "spec" (see also spectrum) with components

`freq`	the Fourier frequencies (in $(0,\pi)$ ) at which the spectrum is computed, see `freq` in `specFGN`.
`spec`	the scaled values spectral density $f(\lambda)$ values at the `freq` values of $\lambda$ . $f^(\lambda) = f(\lambda) / \theta_1$ adjusted such $\int \log(f^(\lambda)) d\lambda = 0$ .
`theta1`	the scale factor $\theta_1$ .
`pq`	a vector of length two, `= c(p,q)`.
`eta`	a named vector `c(H=H, phi=phi, psi=psi)` from input.
`method`	a character indicating the kind of model used.

Author(s)

Jan Beran (principal) and Martin Maechler (fine tuning)

References

Beran (1994) and more, see ....

Examples

 str(r.7  <- specARIMA(0.7, m = 256, p = 0, q = 0))
 str(r.5  <- specARIMA(eta = c(H = 0.5, phi=c(-.06, 0.42, -0.36), psi=0.776),
                       m = 256, p = 3, q = 1))
 plot(r.7)
 plot(r.5)
str(r.7  <- specARIMA(0.7, m = 256, p = 0, q = 0))
 str(r.5  <- specARIMA(eta = c(H = 0.5, phi=c(-.06, 0.42, -0.36), psi=0.776),
                       m = 256, p = 3, q = 1))
 plot(r.7)
 plot(r.5)

Spectral Density of Fractional Gaussian Noise

Description

Calculation of the spectral density $f$ of normalized fractional Gaussian noise with self-similarity parameter $H$ at the Fourier frequencies 2*pi*j/m (j=1,...,(m-1)).

B.specFGN computes (approximations of) the $B(\lambda, H)$ component of the spectrum $f_H(\lambda)$ .

Usage

  specFGN(eta, m, ...)
B.specFGN(lambd, H, k.approx=3, adjust = (k.approx == 3), nsum = 200)
specFGN(eta, m, ...)
B.specFGN(lambd, H, k.approx=3, adjust = (k.approx == 3), nsum = 200)

Arguments

`eta`	parameter vector `eta = c(H, *)`.
`m`	sample size determining Fourier frequencies.
`...`	optional arguments for `B.specFGN()`: `k.approx`, etc
`lambd`	numeric vector of frequencies in [0, pi]
`H`	Hurst parameter in $(\frac 1 2, 1)$ , (can be outside, here).
`k.approx`	either integer (the order of the Paxson approximation), or `NULL`, `NA` for choosing to use the slow direct sum (of `nsum` terms.)
`adjust`	logical indicating (only for `k.approx == 3`, the default) that Paxson's empirical adjustment should also be used.
`nsum`	if the slow sum is used (e.g. for k.approx = NA), the number of terms.

Details

Note that

cov(X(t),X(t+k)) = integral[ exp(iuk)f(u)du ]
f=theta1*spec and integral[log(spec)]=0.

Since longmemo version 1.1-0, a fast approximation is available (and default), using k.approx terms and an adjustment (adjust=TRUE in the default case of k.approx=3), which is due to the analysis and S code from Paxson (1997).

Value

specFGN() returns an object of class "spec" (see also spectrum) with components

`freq`	the Fourier frequencies $\omega_j \in (0,\pi)$ ) at which the spectrum is computed. Note that $omega_j = 2\pi j/m$ for $j=1,..., m-1$ , and $m = \left\lfloor\frac{n-1}{2}\right\rfloor$ .
`spec`	the scaled values spectral density $f(\lambda)$ values at the `freq` values of $\lambda$ . $f^(\lambda) = f(\lambda) / \theta_1$ adjusted such $\int \log(f^(\lambda)) d\lambda = 0$ .
`theta1`	the scale factor $\theta_1$ .
`H`	the self-similarity parameter from input.
`method`	a character indicating the kind of model used.

B.specFGN() returns a vector of (approximate) values $B(\lambda, H)$ .

Author(s)

Jan Beran originally (using the slow sum); Martin Maechler, based on Vern Paxson (1997)'s code.

References

Jan Beran (1994). Statistics for Long-Memory Processes; Chapman & Hall, NY.

Vern Paxson (1997). Fast, Approximate Synthesis of Fractional Gaussian Noise for Generating Self-Similar Network Traffic; Computer Communications Review 27 5, 5–18.

Examples

 str(rg.7  <- specFGN(0.7, m = 100))
 str(rg.5  <- specFGN(0.5, m = 100))# { H = 0.5 <--> white noise ! }

 plot(rg.7) ## work around plot.spec() `bug' in R < 1.6.0
 plot(rg.5, add = TRUE, col = "blue")
 text(2, mean(rg.5$spec), "H = 0.5 [white noise]", col = "blue", adj = c(0,-1/4))
## This was the original method in longmemo, upto version 1.0-0 (incl):
 rg.7.o <- specFGN(0.7, m = 100, k.approx=NA, nsum = 200)
 ## quite accurate (but slightly slower):
 rg.7f  <- specFGN(0.7, m = 100, k.approx=NA, nsum = 10000)
 ## comparing old and new default :
 all.equal(rg.7, rg.7.o)# different in about 5th digit
 all.equal(rg.7, rg.7f )# ==> new default is *more* accurate: 1.42 e-6
 ## takes about  7 sec {in 2011}:
 rg.7ff <- specFGN(0.7, m = 100, k.approx=NA, nsum = 500000)
 all.equal(rg.7f, rg.7ff)# ~ 10 ^ -7
 all.equal(rg.7  $spec, rg.7ff$spec)# ~ 1.33e-6 -- Paxson is accurate!
 all.equal(rg.7.o$spec, rg.7ff$spec)# ~ 2.40e-5 -- old default is less so

str(rg.7  <- specFGN(0.7, m = 100))
 str(rg.5  <- specFGN(0.5, m = 100))# { H = 0.5 <--> white noise ! }

 plot(rg.7) ## work around plot.spec() `bug' in R < 1.6.0
 plot(rg.5, add = TRUE, col = "blue")
 text(2, mean(rg.5$spec), "H = 0.5 [white noise]", col = "blue", adj = c(0,-1/4))
## This was the original method in longmemo, upto version 1.0-0 (incl):
 rg.7.o <- specFGN(0.7, m = 100, k.approx=NA, nsum = 200)
 ## quite accurate (but slightly slower):
 rg.7f  <- specFGN(0.7, m = 100, k.approx=NA, nsum = 10000)
 ## comparing old and new default :
 all.equal(rg.7, rg.7.o)# different in about 5th digit
 all.equal(rg.7, rg.7f )# ==> new default is *more* accurate: 1.42 e-6
 ## takes about  7 sec {in 2011}:
 rg.7ff <- specFGN(0.7, m = 100, k.approx=NA, nsum = 500000)
 all.equal(rg.7f, rg.7ff)# ~ 10 ^ -7
 all.equal(rg.7  $spec, rg.7ff$spec)# ~ 1.33e-6 -- Paxson is accurate!
 all.equal(rg.7.o$spec, rg.7ff$spec)# ~ 2.40e-5 -- old default is less so

Video VBR data

Description

Amount of coded information (variable bit rate) per frame for a certain video sequence. There were about 25 frames per second.

Usage

data(videoVBR)data(videoVBR)

Format

a time-series of length 1000.

References

Heeke, H. (1991) Statistical multiplexing gain for variable bit rate codecs in ATM networks. Int. J. Digit. Analog. Commun. Syst. 4, 261–268.

Heyman, D., Tabatabai, A., and Lakshman, T.V. (1991) Statistical analysis and simulation of video teleconferencing in ATM networks. IEEE Trans. Circuits. Syst. Video Technol. 2, 49–59.

Jan Beran (1994). Dataset no. 2, p.22–23.

Examples

data(videoVBR)
plot(log(videoVBR), main="VBR Data (log)")
data(videoVBR)
plot(log(videoVBR), main="VBR Data (log)")

Whittle Estimator for Fractional Gaussian Noise / Fractional ARIMA

Description

Computes Whittle's approximate MLE for fractional Gaussian noise or fractional ARIMA (=: fARIMA) models, according to Beran's prescript.

Relies on minmizing Qeta() ( $= \tilde{Q}(\eta)$ , which itself is based on the “true” spectrum of the corresponding process; for the spectrum, either specFGN or specARIMA is used.

Usage

WhittleEst(x,
           periodogr.x = per(if(scale) x/sd(x) else x)[2:((n+1) %/% 2)],
           n = length(x), scale = FALSE,
           model = c("fGn", "fARIMA"),
           p, q,
           start = list(H= 0.5, AR= numeric(), MA=numeric()),
           verbose = getOption("verbose"))

## S3 method for class 'WhittleEst'
print(x, digits = getOption("digits"), ...)
WhittleEst(x,
           periodogr.x = per(if(scale) x/sd(x) else x)[2:((n+1) %/% 2)],
           n = length(x), scale = FALSE,
           model = c("fGn", "fARIMA"),
           p, q,
           start = list(H= 0.5, AR= numeric(), MA=numeric()),
           verbose = getOption("verbose"))

## S3 method for class 'WhittleEst'
print(x, digits = getOption("digits"), ...)

Arguments

`x`	numeric vector representing a time series. Maybe omitted if `periodogr.x` and `n` are specified instead.
`periodogr.x`	the (raw) periodogram of `x`; the default, as by Beran, uses `per`, but tapering etc may be an alternative, see also `spec.pgram`.
`n`	length of the time series, `length(x)`.
`scale`	logical indicating if `x` should be standardized to (`sd`) scale 1; originally, `scale = TRUE` used to be built-in; for compatibility with other methods, notably plotting spectra, `scale = FALSE` seems a more natural default.
`model`	numeric vector representing a time series.
`p`, `q`	optional integers specifying the AR and MA orders of the fARIMA model, i.e., only applicable when `model` is `"fARIMA"`.
`start`	list of starting values; currently necessary for `model = "fARIMA"` and with a reasonable default for `model = "fGn"`.
`verbose`	logical indicating if iteration output should be printed.
`digits`, `...`	optional arguments for `print` method, see `print.default`.

Value

An object of class WhittleEst which is basically a list with components

`call`	the function `call`.
`model`	= input
`n`	time series length `length(x)`.
`p`, `q`	for "fARIMA": order of AR and MA parts, respectively.
`coefficients`	numeric 4-column matrix of coefficients with estimate of the full parameter vector $\eta$ , its standard error estimates, z- and P-values. This includes the Hurst parameter $H$ .
`theta1`	the scale parameter $\hat{\theta_1}$ , see `Qeta`.
`vcov`	the variance-covariance matrix for $\eta$ .
`periodogr.x`	= input (with default).
`spec`	the spectral estimate $\hat{f}(\omega_j)$ .

There is a print method, and coef, confint or vcov methods work as well for objects of class "WhittleEst".

Author(s)

Martin Maechler, based on Beran's “main program” in Beran(1994).

References

Beran, Jan (1994). Statistics for Long-Memory Processes; Chapman & Hall. (Section 6.1, p.116–119; 12.1.3, p.223 ff)

Examples

data(NileMin)
(f.Gn.N  <- WhittleEst(NileMin))                             # H = 0.837
(f.A00.N <- WhittleEst(NileMin, model = "fARIMA", p=0, q=0)) # H = 0.899
confint(f.Gn.N)
confint(f.A00.N)

data(videoVBR)
(f.GN    <- WhittleEst(videoVBR))

## similar {but faster !}:
(f.am00  <- WhittleEst(videoVBR, model = "fARIMA", p=0, q=0))
rbind(f.am00$coef,
      f.GN  $coef)# really similar

f.am11  <- WhittleEst(videoVBR, model = "fARIMA",
                      start= list(H= .5, AR = .5, MA= .5))
f.am11
vcov(f.am11)

op <- if(require("sfsmisc"))
  mult.fig(3, main = "Whittle Estimators for videoVBR data")$old.par  else
  par(mar = c(3,1), mgp = c(1.5, 0.6, 0), mar = c(4,4,2,1)+.1)
plot(f.GN)
plot(f.am00)
plot(f.am11)

et <- as.list(coef(f.am11))
et$AR <- c(et$AR, 0, 0) # two more AR coefficients ..
f.am31  <- WhittleEst(videoVBR, model = "fARIMA", start = et)
## ... warning non nonconvergence,  but "kind of okay":
lines(f.am31, col = "red3") ## drawing on top of ARMA(1,1) above - *small* diff

f.am31 # not all three are "significant"
round(cov2cor(vcov(f.am31)), 3) # and they are highly correlated

et <- as.list(coef(f.am31))
et$AR <- unname(unlist(et[c("AR1", "AR2")]))
f.am21  <- WhittleEst(videoVBR, model = "fARIMA",
                      start = c(et[c("H","AR", "MA")]))
f.am21
lines(f.am21, col = adjustcolor("gold", .4), lwd=4)

par(op)## (reset graphic layout)

##--> ?plot.WhittleEst  for an example using  'periodogr.x'
data(NileMin)
(f.Gn.N  <- WhittleEst(NileMin))                             # H = 0.837
(f.A00.N <- WhittleEst(NileMin, model = "fARIMA", p=0, q=0)) # H = 0.899
confint(f.Gn.N)
confint(f.A00.N)

data(videoVBR)
(f.GN    <- WhittleEst(videoVBR))

## similar {but faster !}:
(f.am00  <- WhittleEst(videoVBR, model = "fARIMA", p=0, q=0))
rbind(f.am00$coef,
      f.GN  $coef)# really similar

f.am11  <- WhittleEst(videoVBR, model = "fARIMA",
                      start= list(H= .5, AR = .5, MA= .5))
f.am11
vcov(f.am11)

op <- if(require("sfsmisc"))
  mult.fig(3, main = "Whittle Estimators for videoVBR data")$old.par  else
  par(mar = c(3,1), mgp = c(1.5, 0.6, 0), mar = c(4,4,2,1)+.1)
plot(f.GN)
plot(f.am00)
plot(f.am11)

et <- as.list(coef(f.am11))
et$AR <- c(et$AR, 0, 0) # two more AR coefficients ..
f.am31  <- WhittleEst(videoVBR, model = "fARIMA", start = et)
## ... warning non nonconvergence,  but "kind of okay":
lines(f.am31, col = "red3") ## drawing on top of ARMA(1,1) above - *small* diff

f.am31 # not all three are "significant"
round(cov2cor(vcov(f.am31)), 3) # and they are highly correlated

et <- as.list(coef(f.am31))
et$AR <- unname(unlist(et[c("AR1", "AR2")]))
f.am21  <- WhittleEst(videoVBR, model = "fARIMA",
                      start = c(et[c("H","AR", "MA")]))
f.am21
lines(f.am21, col = adjustcolor("gold", .4), lwd=4)

par(op)## (reset graphic layout)

##--> ?plot.WhittleEst  for an example using  'periodogr.x'

Package 'longmemo'

Help Index

Covariance for fractional ARIMA

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Covariance Matrix of Eta for Fractional Gaussian Noise

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Covariances of a Fractional ARIMA(0,d,0) Process

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Covariances of a Fractional Gaussian Process

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Ethernet Traffic Data Set

Description

Usage

Format

Source

Examples

Fractional EXP (FEXP) Model Estimator

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Log-Log and Log-X Plot of Spectrum

Description

Usage

Arguments

Author(s)

See Also

NBS measurement deviations from 1 kg

Description

Usage

Format

Source

References

Examples

Northern Hemisphere Temperature

Description

Usage

Format

Source

References

Examples

Nile River Minima, yearly 622–1284

Description

Usage

Format

Source

References

Examples