Package 'mar1s' reference manual

Title:	Multiplicative AR(1) with Seasonal Processes
Description:	Multiplicative AR(1) with Seasonal is a stochastic process model built on top of AR(1). The package provides the following procedures for MAR(1)S processes: fit, compose, decompose, advanced simulate and predict.
Authors:	Andrey Paramonov
Maintainer:	Andrey Paramonov <[email protected]>
License:	GPL (>= 3)
Version:	2.1.1
Built:	2025-02-07 05:27:50 UTC
Source:	https://github.com/aparamon/mar1s

Compose and Decompose AR(1) Process

Description

compose.ar1 composes AR(1) process realization by given vector(s) of innovations.

decompose.ar1 extracts AR(1) process residuals from time series.

Usage

compose.ar1(arcoef, innov, init = 0, xregcoef = 0, xreg = NULL,
            init.xreg = rep(0, length(xregcoef)))

decompose.ar1(arcoef, data, init = NA, xregcoef = 0, xreg = NULL,
              init.xreg = rep(NA, length(xregcoef)))
compose.ar1(arcoef, innov, init = 0, xregcoef = 0, xreg = NULL,
            init.xreg = rep(0, length(xregcoef)))

decompose.ar1(arcoef, data, init = NA, xregcoef = 0, xreg = NULL,
              init.xreg = rep(NA, length(xregcoef)))

Arguments

`arcoef`	A number specifying autoregression coefficient.
`innov`	A univariate or multivariate time series containing the innovations.
`data`	A univariate or multivariate time series containing the process realization(s).
`init`	A number specifying the value of the process just prior to the start value in `innov`/`data`.
`xregcoef`	A vector specifying coefficients for the external regressors.
`xreg`	A matrix-like object of the same row count as `innov`/`data`, specifying the values of external regressors. The default `NULL` means zeroes.
`init.xreg`	A vector specifying the values of external regressors just prior to the start values in `xreg`. The default `NULL` means zeroes.

Details

Here AR(1) process with external regressors is a linear regresson with AR(1) model for the error term:

$y_t = b_1 x_{t, 1} + \dots + b_k x_{t, k} + z_t$

$z_t = a z_{t-1} + e_t$

Use xreg = NULL for the regular AR(1) process.

Value

An object of the same type and dimensions as innov/data (typically time series).

Examples

## Simple
e <- ts(c(0, 1, 0, 1, 0), freq = 12)
compose.ar1(0.1, e)
compose.ar1(0.1, e, 1)

x <- ts(c(0, 1, 0, 1, 0), freq = 12)
decompose.ar1(0.1, x)
decompose.ar1(0.1, x, 1)

## Multiseries
compose.ar1(0.1, ts(cbind(0, 1)))
compose.ar1(0.1, ts(cbind(c(0, 1, 0), c(1, 0, 1))))

decompose.ar1(0.1, ts(cbind(0, 1)))
decompose.ar1(0.1, ts(cbind(c(0, 1, 0), c(1, 0, 1))))

## External regressors
xreg1 <- rep(2, 5)
xreg2 <- matrix(rep(c(2, 1), each = 5), 5, 2)
e <- ts(c(0, 1, 0, 1, 0), freq = 12)
compose.ar1(0.1, e, xregcoef = 0.5, xreg = xreg1)
compose.ar1(0.1, e, xregcoef = 0.5, init = 0, xreg = xreg1, init.xreg = -2)
compose.ar1(0.1, e, xregcoef = c(1, -1), xreg = xreg2)

x <- ts(c(0, 1, 0, 1, 0), freq = 12)
decompose.ar1(0.1, x, xregcoef = 0.5, xreg = xreg1)
decompose.ar1(0.1, x, xregcoef = 0.5, init = 0, xreg = xreg1, init.xreg = -2)
decompose.ar1(0.1, x, xregcoef = c(1, -1), xreg = xreg2)

## Back-test
a <- 0.5
innov <- ts(rnorm(10), frequency = 12)
init <- 1
xrcoef <- seq(-0.1, 0.1, length.out = 3)
xreg <- matrix(1:30, 10, 3)
init.xreg <- 1:3
x <- compose.ar1(a, innov, init, xrcoef, xreg, init.xreg)
r <- decompose.ar1(a, x, init, xrcoef, xreg, init.xreg)
stopifnot(all.equal(innov, r))
## Simple
e <- ts(c(0, 1, 0, 1, 0), freq = 12)
compose.ar1(0.1, e)
compose.ar1(0.1, e, 1)

x <- ts(c(0, 1, 0, 1, 0), freq = 12)
decompose.ar1(0.1, x)
decompose.ar1(0.1, x, 1)

## Multiseries
compose.ar1(0.1, ts(cbind(0, 1)))
compose.ar1(0.1, ts(cbind(c(0, 1, 0), c(1, 0, 1))))

decompose.ar1(0.1, ts(cbind(0, 1)))
decompose.ar1(0.1, ts(cbind(c(0, 1, 0), c(1, 0, 1))))

## External regressors
xreg1 <- rep(2, 5)
xreg2 <- matrix(rep(c(2, 1), each = 5), 5, 2)
e <- ts(c(0, 1, 0, 1, 0), freq = 12)
compose.ar1(0.1, e, xregcoef = 0.5, xreg = xreg1)
compose.ar1(0.1, e, xregcoef = 0.5, init = 0, xreg = xreg1, init.xreg = -2)
compose.ar1(0.1, e, xregcoef = c(1, -1), xreg = xreg2)

x <- ts(c(0, 1, 0, 1, 0), freq = 12)
decompose.ar1(0.1, x, xregcoef = 0.5, xreg = xreg1)
decompose.ar1(0.1, x, xregcoef = 0.5, init = 0, xreg = xreg1, init.xreg = -2)
decompose.ar1(0.1, x, xregcoef = c(1, -1), xreg = xreg2)

## Back-test
a <- 0.5
innov <- ts(rnorm(10), frequency = 12)
init <- 1
xrcoef <- seq(-0.1, 0.1, length.out = 3)
xreg <- matrix(1:30, 10, 3)
init.xreg <- 1:3
x <- compose.ar1(a, innov, init, xrcoef, xreg, init.xreg)
r <- decompose.ar1(a, x, init, xrcoef, xreg, init.xreg)
stopifnot(all.equal(innov, r))

Compose and Decompose MAR(1)S Process

Description

compose.mar1s composes MAR(1)S process realization by given vector of log-innovations.

decompose.mar1s extracts MAR(1)S process components from time series.

Usage

compose.mar1s(object, loginnov, start.time = head(time(loginnov), 1),
              xreg.absdata = NULL, init.absdata = NULL)

decompose.mar1s(object, absdata, start.time = head(time(absdata), 1),
                init.absdata = rep(NA, NCOL(absdata)))
compose.mar1s(object, loginnov, start.time = head(time(loginnov), 1),
              xreg.absdata = NULL, init.absdata = NULL)

decompose.mar1s(object, absdata, start.time = head(time(absdata), 1),
                init.absdata = rep(NA, NCOL(absdata)))

Arguments

`object`	An object of class `"mar1s"` specifying the model parameters.
`loginnov`	A univariate time series containing the log-innovations.
`absdata`	A univariate or multivariate time series containing the process realization.
`start.time`	The sampling time for the first simulation step.
`xreg.absdata`	A matrix-like object with row count = `n.ahead`, specifying the values for the external regressors. If `NULL`, default values are used.
`init.absdata`	A vector specifying the initial values of the process. If `NULL`, default values are used.

Details

Multiplicative AR(1) with Seasonal (MAR(1)S) process is defined as

$x_t = exp(s_t + y_t)$

$y_{t, 1} = b_1 y_{t, 2} + \dots + b_k y_{t, k + 1} + z_t$

$z_t = a z_{t-1} + e_t$

where

$s_t$ is the log-seasonal component,

$y_t$ is the AR(1) (log-stochastic) component,

$e_t$ is the log-residuals (random component).

Value

`absdata`	Realization of the process (a univariate or multivariate time series object).
`logdata`	Logarithm of `absdata` (a univariate or multivariate time series object).
`logstoch`	Log-stochastic component (a univariate or multivariate time series object).
`logresid`	Random component (a univariate time series object).

Note

decompose.mar1s and fit.mar1s compute the random component in different ways: decompose.mar1s uses filter while fit.mar1s saves the residuals returned by arima. The results may be different in:

the first value:: decompose.mar1s uses specified init.absdata while arima always assumes zero initial values for the fitted process;
non-finite values:: decompose.mar1s handles non-finite values more accurately.

Examples

data(forest.fire, package = "mar1s")
data(nesterov.index, package = "mar1s")

## Simple
mar1s <- fit.mar1s(window(forest.fire, 1969, 1989))

x <- ts(rnorm(365, sd = mar1s$logresid.sd), start = c(1989, 1))
plot(compose.mar1s(mar1s, x)$absdata)

decomp <- decompose.mar1s(mar1s, mar1s$decomposed$absdata)
delta <- abs(nan2na(mar1s$decomposed$logresid) -
             nan2na(decomp$logresid))
stopifnot(all(na.exclude(tail(delta, -1)) < 1E-6))

## External regressors
mar1s <- fit.mar1s(window(forest.fire, 1969, 1989),
                   window(nesterov.index[, "mean"], 1969, 1989))

x <- rnorm(365, sd = mar1s$logresid.sd)
xreg <- window(nesterov.index[, "mean"], 1989.001, 1990)
plot(compose.mar1s(mar1s, x, c(1989, 1), xreg)$absdata)

decomp <- decompose.mar1s(mar1s, mar1s$decomposed$absdata)
delta <- abs(mar1s$decomposed$logstoch - decomp$logstoch)
stopifnot(all(na.exclude(delta) < 1E-6))
delta <- abs(nan2na(mar1s$decomposed$logresid) -
             nan2na(decomp$logresid))
stopifnot(all(na.exclude(tail(delta, -1)) < 1E-6))
data(forest.fire, package = "mar1s")
data(nesterov.index, package = "mar1s")

## Simple
mar1s <- fit.mar1s(window(forest.fire, 1969, 1989))

x <- ts(rnorm(365, sd = mar1s$logresid.sd), start = c(1989, 1))
plot(compose.mar1s(mar1s, x)$absdata)

decomp <- decompose.mar1s(mar1s, mar1s$decomposed$absdata)
delta <- abs(nan2na(mar1s$decomposed$logresid) -
             nan2na(decomp$logresid))
stopifnot(all(na.exclude(tail(delta, -1)) < 1E-6))

## External regressors
mar1s <- fit.mar1s(window(forest.fire, 1969, 1989),
                   window(nesterov.index[, "mean"], 1969, 1989))

x <- rnorm(365, sd = mar1s$logresid.sd)
xreg <- window(nesterov.index[, "mean"], 1989.001, 1990)
plot(compose.mar1s(mar1s, x, c(1989, 1), xreg)$absdata)

decomp <- decompose.mar1s(mar1s, mar1s$decomposed$absdata)
delta <- abs(mar1s$decomposed$logstoch - decomp$logstoch)
stopifnot(all(na.exclude(delta) < 1E-6))
delta <- abs(nan2na(mar1s$decomposed$logresid) -
             nan2na(decomp$logresid))
stopifnot(all(na.exclude(tail(delta, -1)) < 1E-6))

Fit Multiplicative AR(1) with Seasonal Process to Data

Description

Fits Multiplicative AR(1) with Seasonal process model to time series.

Usage

fit.mar1s(x, xreg = NULL, seasonal.fun = seasonal.smooth, ...)
fit.mar1s(x, xreg = NULL, seasonal.fun = seasonal.smooth, ...)

Arguments

`x`	A univariate time series.
`xreg`	A univariate or multivariate time series of external regressors, or `NULL`.
`seasonal.fun`	A function which takes a univariate time series as its first argument and returns the estimated seasonal component.
`...`	Additional arguments passed to `seasonal.fun`.

Value

An object of class "mar1s" with the following components:

`logseasonal`	Estimated log-seasonal figure (a univariate or multivariate time series object).
`logstoch.ar1`	AR(1) with external regressors model fitted for the log-stochastic component (an object of class `"Arima"`).
`logresid.sd`	Standard deviation of the residuals.
`decomposed`	An object of class `"mar1s.ts"` containing decomposed time series (see `compose.mar1s`).

Examples

data(forest.fire, package = "mar1s")
data(nesterov.index, package = "mar1s")

## Simple
mar1s <- fit.mar1s(forest.fire)
plot(mar1s$logseasonal)
confint(mar1s$logstoch.ar1)
mar1s$logresid.sd
resid <- nan2na(mar1s$decomposed$logresid)
qqnorm(resid)
qqline(resid)

## External regressors
mar1s <- fit.mar1s(forest.fire, nesterov.index[, "mean"])
plot(cbind(mar1s$logseasonal, mar1s$logseasonal.r))
confint(mar1s$logstoch.ar1)
resid <- nan2na(mar1s$decomposed$logresid)
qqnorm(resid)
qqline(resid)
data(forest.fire, package = "mar1s")
data(nesterov.index, package = "mar1s")

## Simple
mar1s <- fit.mar1s(forest.fire)
plot(mar1s$logseasonal)
confint(mar1s$logstoch.ar1)
mar1s$logresid.sd
resid <- nan2na(mar1s$decomposed$logresid)
qqnorm(resid)
qqline(resid)

## External regressors
mar1s <- fit.mar1s(forest.fire, nesterov.index[, "mean"])
plot(cbind(mar1s$logseasonal, mar1s$logseasonal.r))
confint(mar1s$logstoch.ar1)
resid <- nan2na(mar1s$decomposed$logresid)
qqnorm(resid)
qqline(resid)

Forest fire in Irkutsk region, USSR: historical data

Description

Number of forest fire seats in Irkutsk region, USSR. Daily from April 01 to October 31, 1969–1991 (total 4708 observations).

Usage

data(forest.fire)data(forest.fire)

Format

A univariate time series.

Source

AviaLesoOkhrana (http://www.nffc.aviales.ru/engl/main.sht).

Examples

data(forest.fire)
colnames(forest.fire)
plot(forest.fire)
data(forest.fire)
colnames(forest.fire)
plot(forest.fire)

Nesterov index in Irkutsk region, USSR: historical data

Description

Values of Nesterov index in 6 distincts of Irkutsk region, USSR + region averaged. Daily from April 01 to October 31, 1969–1991 (total 4708 observations).

Usage

data(nesterov.index)data(nesterov.index)

Format

A multivariate time series.

Details

The Nesterov index is the official Russian fire-danger rating specified by standard GOST R 22.1.09–99. It is calculated using the ignition index, the temperature, the dew-point temperature and the number of days since last significant ( $\ge 3 mm$ ) precipitation.

The dataset consists of daily values 1969–1991.

Source

Russian Institute of Hydrometeorogical Information–World Data Center (http://meteo.ru/english/).

Examples

data(nesterov.index)
colnames(nesterov.index)
plot(nesterov.index)
data(nesterov.index)
colnames(nesterov.index)
plot(nesterov.index)

Averaged Seasonal Component of Time Series

Description

Extracts seasonal component of time series by averaging observations on the same position in the cycle.

Usage

seasonal.ave(x, ave.FUN = mean, ...)
seasonal.ave(x, ave.FUN = mean, ...)

Arguments

`x`	A univariate time series.
`ave.FUN`	Averaging function.
`...`	Additional arguments passed to `ave.FUN`.

Value

A time series object with times from 0 to 1 and the same frequency as x.

Examples

set.seed(19860306)

## Artificial example
x <- ts(sin(2*pi*(3:97)/10) + 0.5*rnorm(length(3:97)),
	start = c(0, 3), frequency = 10)

plot.default(time(x)%%1, x, xlab = "Phase")
lines(seasonal.ave(x), col = "blue")
lines(seasonal.ave(x, median), col = "green")
legend("bottomleft",
       legend = c("Mean averaging",
		  "Median averaging"),
       col = c("blue", "green"),
       lty = "solid")

## Realistic example
data(nesterov.index, package = "mar1s")
x <- log(nesterov.index[, "mean"])
x[x < -10] <- -Inf

plot.default(time(x)%%1, x, xlab = "Phase", pch = ".")
lines(seasonal.ave(x), col = "blue")
lines(seasonal.ave(x, median), col = "green")
legend("topleft",
       legend = c("Mean averaging",
		  "Median averaging"),
       col = c("blue", "green"),
       lty = "solid")
set.seed(19860306)

## Artificial example
x <- ts(sin(2*pi*(3:97)/10) + 0.5*rnorm(length(3:97)),
	start = c(0, 3), frequency = 10)

plot.default(time(x)%%1, x, xlab = "Phase")
lines(seasonal.ave(x), col = "blue")
lines(seasonal.ave(x, median), col = "green")
legend("bottomleft",
       legend = c("Mean averaging",
		  "Median averaging"),
       col = c("blue", "green"),
       lty = "solid")

## Realistic example
data(nesterov.index, package = "mar1s")
x <- log(nesterov.index[, "mean"])
x[x < -10] <- -Inf

plot.default(time(x)%%1, x, xlab = "Phase", pch = ".")
lines(seasonal.ave(x), col = "blue")
lines(seasonal.ave(x, median), col = "green")
legend("topleft",
       legend = c("Mean averaging",
		  "Median averaging"),
       col = c("blue", "green"),
       lty = "solid")

Smooth Seasonal Component of Time Series

Description

Extracts seasonal component of time series by fitting the data with a linear combination of smooth periodic functions.

Usage

seasonal.smooth(x, basis = create.fourier.basis(nbasis = 3), lambda = 0, ...)
seasonal.smooth(x, basis = create.fourier.basis(nbasis = 3), lambda = 0, ...)

Arguments

`x`	A univariate time series.
`basis`	A functional basis object (see `basisfd`). By default, use a linear combination of `const`, `sin((2pi)t)` and `cos((2pi)t)`.
`lambda`	A nonnegative number specifying the amount of smoothing. By default, apply no additional smoothing.
`...`	Not currently used.

Details

Although it is possible to specify arbitrary functional basis object, the function will only work properly if the basis is periodical on the unit interval. It is recommended to use a Fourier basis with default period (create.fourier.basis).

Positive values of lambda imply a restriction on roughness of the result. The more the value, the more smooth result is; see smooth.basis for more detailed description.

Value

A time series object with times from 0 to 1 and the same frequency as x. The smoothing functional data object is stored in attribute fd.

Examples

set.seed(19860306)

## Artificial example
x <- ts(sin(2*pi*(3:97)/10) + 0.5*rnorm(length(3:97)),
	start = c(0, 3), frequency = 10)

fourier3 <- seasonal.smooth(x)
fourier9 <- seasonal.smooth(x, create.fourier.basis(nbasis = 9))
fourier9s<- seasonal.smooth(x, create.fourier.basis(nbasis = 9), 1E-6)

plot.default(time(x)%%1, x, xlab = "Phase")
points(fourier3, pch = 20,  col = "blue")
lines(attr(fourier3, "fd"), col = "blue")
points(fourier9, pch = 20,  col = "green")
lines(attr(fourier9, "fd"), col = "green")
points(fourier9s,pch = 20,  col = "red")
lines(attr(fourier9s, "fd"),col = "red")
legend("bottomleft",
       legend = c("Fourier-3 basis",
		  "Fourier-9 basis",
		  "Fourier-9 basis, smooth"),
       col = c("blue", "green", "red"),
       lty = "solid")

## Realistic example
data(nesterov.index, package = "mar1s")
x <- log(nesterov.index[, "mean"])
x[x < -10] <- -Inf

fourier3 <- seasonal.smooth(x)
fourier9 <- seasonal.smooth(x, create.fourier.basis(nbasis = 9))
fourier9s<- seasonal.smooth(x, create.fourier.basis(nbasis = 9), 2E-5)

plot.default(time(x)%%1, x, xlab = "Phase", pch = ".")
lines(attr(fourier3, "fd"), col = "blue")
lines(attr(fourier9, "fd"), col = "green")
lines(attr(fourier9s,"fd"), col = "red")
legend("topleft",
       legend = c("Fourier-3 basis",
		  "Fourier-9 basis",
		  "Fourier-9 basis, smooth"),
       col = c("blue", "green", "red"),
       lty = "solid")
set.seed(19860306)

## Artificial example
x <- ts(sin(2*pi*(3:97)/10) + 0.5*rnorm(length(3:97)),
	start = c(0, 3), frequency = 10)

fourier3 <- seasonal.smooth(x)
fourier9 <- seasonal.smooth(x, create.fourier.basis(nbasis = 9))
fourier9s<- seasonal.smooth(x, create.fourier.basis(nbasis = 9), 1E-6)

plot.default(time(x)%%1, x, xlab = "Phase")
points(fourier3, pch = 20,  col = "blue")
lines(attr(fourier3, "fd"), col = "blue")
points(fourier9, pch = 20,  col = "green")
lines(attr(fourier9, "fd"), col = "green")
points(fourier9s,pch = 20,  col = "red")
lines(attr(fourier9s, "fd"),col = "red")
legend("bottomleft",
       legend = c("Fourier-3 basis",
		  "Fourier-9 basis",
		  "Fourier-9 basis, smooth"),
       col = c("blue", "green", "red"),
       lty = "solid")

## Realistic example
data(nesterov.index, package = "mar1s")
x <- log(nesterov.index[, "mean"])
x[x < -10] <- -Inf

fourier3 <- seasonal.smooth(x)
fourier9 <- seasonal.smooth(x, create.fourier.basis(nbasis = 9))
fourier9s<- seasonal.smooth(x, create.fourier.basis(nbasis = 9), 2E-5)

plot.default(time(x)%%1, x, xlab = "Phase", pch = ".")
lines(attr(fourier3, "fd"), col = "blue")
lines(attr(fourier9, "fd"), col = "green")
lines(attr(fourier9s,"fd"), col = "red")
legend("topleft",
       legend = c("Fourier-3 basis",
		  "Fourier-9 basis",
		  "Fourier-9 basis, smooth"),
       col = c("blue", "green", "red"),
       lty = "solid")

Simulate from MAR(1)S Process

Description

sim.mar1s simulates from MAR(1)S process.

predict.mar1s is a wrapper around sim.mar1s which estimates confidence intervals for the future values of the MAR(1)S process.

Usage

sim.mar1s(object, n.ahead = 1, n.sim = 1, start.time = 0,
          xreg.absdata = NULL, init.absdata = NULL)

## S3 method for class 'mar1s'
predict(object, n.ahead = 1, start.time = 0,
        xreg.absdata = NULL, init.absdata = NULL,
        probs = c(0.05, 0.5, 0.95), n.sim = 1000, ...)sim.mar1s(object, n.ahead = 1, n.sim = 1, start.time = 0,
          xreg.absdata = NULL, init.absdata = NULL)

## S3 method for class 'mar1s'
predict(object, n.ahead = 1, start.time = 0,
        xreg.absdata = NULL, init.absdata = NULL,
        probs = c(0.05, 0.5, 0.95), n.sim = 1000, ...)

Arguments

`object`	An object of class `"mar1s"` specifying the model parameters.
`n.ahead`	Number of steps ahead at which to simulate/predict.
`n.sim`	Number of simulations.
`start.time`	The sampling time for the first simulation step.
`xreg.absdata`	A matrix-like object with row count = `n.ahead`, specifying the values for the external regressors. If `NULL`, default values are used.
`init.absdata`	A vector specifying the initial values of the process. If `NULL`, default values are used.
`probs`	A vector of probabilities.
`...`	Arguments from previous methods.

Value

For sim.mar1s, a vector of simulated values.

For predict.mar1s, a vector of estimated quantiles.

Examples

data(forest.fire, package = "mar1s")
data(nesterov.index, package = "mar1s")

## Univariate
mar1s <- fit.mar1s(forest.fire)

sim.mar1s(mar1s)
sim.mar1s(mar1s, n.sim = 6)
sim.mar1s(mar1s, n.ahead = 3)

predict(mar1s)
predict(mar1s, n.ahead = 10)
predict(mar1s, init.absdata = 100)

t <- seq(1/12, 11/12, 1/6) 
p <- mapply(predict, start.time = t,
            MoreArgs = list(object = mar1s, probs = c(0.05, 0.95)))
plot(exp(mar1s$logseasonal), ylim = c(0, max(p)),
     ylab = "Forest fire")
arrows(t, p[1, ], t, p[2, ],
       code = 3, angle = 90, length = 0.05)

## External regressors
mar1s <- fit.mar1s(forest.fire, nesterov.index[, "mean"])

sim.mar1s(mar1s)
sim.mar1s(mar1s, n.sim = 6)

predict(mar1s)
predict(mar1s, xreg.absdata = 10000)
predict(mar1s, init.absdata = c(100, 1000))
data(forest.fire, package = "mar1s")
data(nesterov.index, package = "mar1s")

## Univariate
mar1s <- fit.mar1s(forest.fire)

sim.mar1s(mar1s)
sim.mar1s(mar1s, n.sim = 6)
sim.mar1s(mar1s, n.ahead = 3)

predict(mar1s)
predict(mar1s, n.ahead = 10)
predict(mar1s, init.absdata = 100)

t <- seq(1/12, 11/12, 1/6) 
p <- mapply(predict, start.time = t,
            MoreArgs = list(object = mar1s, probs = c(0.05, 0.95)))
plot(exp(mar1s$logseasonal), ylim = c(0, max(p)),
     ylab = "Forest fire")
arrows(t, p[1, ], t, p[2, ],
       code = 3, angle = 90, length = 0.05)

## External regressors
mar1s <- fit.mar1s(forest.fire, nesterov.index[, "mean"])

sim.mar1s(mar1s)
sim.mar1s(mar1s, n.sim = 6)

predict(mar1s)
predict(mar1s, xreg.absdata = 10000)
predict(mar1s, init.absdata = c(100, 1000))

Package 'mar1s'

Help Index

Compose and Decompose AR(1) Process

Description

Usage

Arguments

Details

Value

See Also

Examples

Compose and Decompose MAR(1)S Process

Description

Usage

Arguments

Details

Value

Note

See Also

Examples

Fit Multiplicative AR(1) with Seasonal Process to Data

Description

Usage

Arguments

Value

See Also

Examples

Forest fire in Irkutsk region, USSR: historical data

Description

Usage

Format

Source

Examples

Nesterov index in Irkutsk region, USSR: historical data

Description

Usage

Format

Details

Source

Examples

Averaged Seasonal Component of Time Series

Description

Usage

Arguments

Value

See Also

Examples

Smooth Seasonal Component of Time Series

Description

Usage

Arguments

Details

Value

See Also

Examples

Simulate from MAR(1)S Process

Description

Usage

Arguments

Value

See Also

Examples