fun.forecast {sarima} | R Documentation |
Forecasting functions for seasonal ARIMA models
Description
Forecasting functions for seasonal ARIMA models.
Usage
fun.forecast(past, n = max(2 * length(past), 12), eps = numeric(n), pasteps, ...)
Arguments
past |
past values of the time series, by default zeroes. |
n |
number of forecasts to compute. |
eps |
values of the white noise sequence (for simulation of future). Currently not used! |
pasteps |
past values of the white noise sequence for models with MA terms, 0 by default. |
... |
specification of the model, passed to |
Details
fun.forecast
computes predictions from a SARIMA model. The
model is specified using the "..." arguments which are passed to
new("SarimaModel", ...)
, see the description of class
"SarimaModel"
for details.
Argument past
, if provided, should contain a least as many values as
needed for the prediction equation. It is harmless to provide more
values than necessary, even a whole time series.
fun.forecast
can be used to illustrate, for example, the
inherent difference for prediction of integrated and seasonally
integrated models to corresponding models with roots close to the unit
circle.
Value
the forecasts as an object of class "ts"
Author(s)
Georgi N. Boshnakov
Examples
f1 <- fun.forecast(past = 1, n = 100, ar = c(0.85), center = 5)
plot(f1)
f2 <- fun.forecast(past = 8, n = 100, ar = c(0.85), center = 5)
plot(f2)
f3 <- fun.forecast(past = 10, n = 100, ar = c(-0.85), center = 5)
plot(f3)
frw1 <- fun.forecast(past = 1, n = 100, iorder = 1)
plot(frw1)
frw2 <- fun.forecast(past = 3, n = 100, iorder = 1)
plot(frw2)
frwa1 <- fun.forecast(past = c(1, 2), n = 100, ar = c(0.85), iorder = 1)
plot(frwa1)
fi2a <- fun.forecast(past = c(3, 1), n = 100, iorder = 2)
plot(fi2a)
fi2b <- fun.forecast(past = c(1, 3), n = 100, iorder = 2)
plot(fi2b)
fari1p2 <- fun.forecast(past = c(0, 1, 3), ar = c(0.9), n = 20, iorder = 2)
plot(fari1p2)
fsi1 <- fun.forecast(past = rnorm(4), n = 100, siorder = 1, nseasons = 4)
plot(fsi1)
fexa <- fun.forecast(past = rnorm(5), n = 100, ar = c(0.85), siorder = 1,
nseasons = 4)
plot(fexa)
fi2a <- fun.forecast(past = rnorm(24, sd = 5), n = 120, siorder = 2,
nseasons = 12)
plot(fi2a)
fi1si1a <- fun.forecast(past = rnorm(24, sd = 5), n = 120, iorder = 1,
siorder = 1, nseasons = 12)
plot(fi1si1a)
fi1si1a <- fun.forecast(past = AirPassengers[120:144], n = 120, iorder = 1,
siorder = 1, nseasons = 12)
plot(fi1si1a)
m1 <- list(iorder = 1, siorder = 1, ma = 0.8, nseasons = 12, sigma2 = 1)
m1
x <- sim_sarima(model = m1, n = 500)
acf(diff(diff(x), lag = 12), lag.max = 96)
pacf(diff(diff(x), lag = 12), lag.max = 96)
m2 <- list(iorder = 1, siorder = 1, ma = 0.8, sma = 0.5, nseasons = 12,
sigma2 = 1)
m2
x2 <- sim_sarima(model = m2, n = 500)
acf(diff(diff(x2), lag = 12), lag.max = 96)
pacf(diff(diff(x2), lag = 12), lag.max = 96)
fit2 <- arima(x2, order = c(0, 1, 1),
seasonal = list(order = c(0, 1, 0), nseasons = 12))
fit2
tsdiag(fit2)
tsdiag(fit2, gof.lag = 96)
x2past <- rnorm(13, sd = 10)
x2 <- sim_sarima(model = m2, n = 500, x = list(init = x2past))
plot(x2)
fun.forecast(ar = 0.5, n = 100)
fun.forecast(ar = 0.5, n = 100, past = 1)
fun.forecast(ma = 0.5, n = 100, past = 1)
fun.forecast(iorder = 1, ma = 0.5, n = 100, past = 1)
fun.forecast(iorder = 1, ma = 0.5, ar = 0.8, n = 100, past = 1)
fun.forecast(m1, n = 100)
fun.forecast(m2, n = 100)
fun.forecast(iorder = 1, ar = 0.8, ma = 0.5, n = 100, past = 1)