glarma (version 1.6-0)

forecast: Forecasting GLARMA time series

Description

forecast is a generic function for forecasting time series or time series models. The function invokes particular methods which depend on the class of the first argument.

Currently the only method provided by the package is for objects of class "glarma".

Usage

forecast(object, ...)
# S3 method for glarma
forecast(object, n.ahead = 1, newdata = 0,
         newoffset = 0, newm = 1, ...)

Arguments

object

An object of class "glarma" obtained from a call to glarma

n.ahead

The number of periods ahead to be forecast.

newdata

The model matrix \(X\) comprising the values of the predictors for the times for which the series is to be predicted.

newoffset

A vector containing the values of the offset for the times for which the series is to be predicted.

newm

A vector containing the number of trials when forecasting binomial or binary time series. Defaults to the binary case.

…

Further arguments for the call, currently unused.

Value

forecast currently has no default method.

When object is of class "glarma", forecast returns an object of class "glarmaForecast" with components:

eta

the forecast values of the regression component of the state variable

W

the forecast values of the state variable \(W\)

mu

the conditional mean \(\mu_t\)

Y

the simulated series based on the fitted model

n.ahead

the number of steps ahead for which the forecasts were requested in the call to forecast

newdata

the model matrix \(X\) comprising the values of the predictors for the times for which the series is to be predicted

newoffset

the vector containing the values of the offset for the times for which the series is to be predicted

newm

the vector giving the number of trials when forecasting binomial or binary time series

model

the "glarma" object from the call to forecast

Details

Only one forecasting method is currently provided, for objects of class "glarma". This produces an object of class "glarmaForecast".

When forecasting one step ahead, the values in the matrix newdata (and offset if there is an offset) in the GLARMA model are used along with the regression coefficients in the model to obtain the predicted value of \(\eta\), the regression component of the state variable \(W\). The predicted value of the ARMA component of the state variable is then added to this value to give the predicted value of \(W\).

When further predictions are required, since no data is available to calculate the predicted value of the state variable, an observation is generated from the predicted distribution and the methodology for one step ahead is then used on this generated data. This process is repeated until predictions are obtained for the required number of time periods (specified by n.ahead). Note that the value of n.ahead must equal the row dimension of newdata and if they are specified, of newoffset and newm.

For completeness a randomly generated value of the time series is produced even for one step-ahead prediction.

Note that the forecasted time series returned as the component fitted is then a randomly generated sample path for the predicted time series. If a sample of such paths is produced by repeated calls to forecast then sample predicted distributions can be obtained for the forecast series.

In the case of binary or binomial time series in addition to values of the predictors in the regression component of the state variable and the values of any offset, the numbers of trials for the binomially distributed future observations are required. This information should be provided in the argument newm. If not, the number of trials defaults to 1, which is the case of binary responses.

Examples

# NOT RUN {
require(zoo)
### Model number of deaths
data(DriverDeaths)
y <- DriverDeaths[, "Deaths"]
X <- as.matrix(DriverDeaths[, 2:5])
Population <- DriverDeaths[, "Population"]

### Offset included
glarmamod <- glarma(y, X, offset = log(Population/100000),
                    phiLags = c(12),
                    thetaLags = c(1),
                    type = "Poi", method = "FS",
                    residuals = "Pearson", maxit = 100, grad = 1e-6)
print(summary(glarmamod))

XT1 <- matrix(X[72,], nrow = 1)
offsetT1 <- log(Population/100000)[72]

mu <- forecast(glarmamod, 1, XT1, offsetT1)$mu
print(mu)


### Save some values
allX <- X
allFits <- fitted(glarmamod)
ally <- y

### Look at a succession of forecasts
### Using actual values in forecasts
forecasts <- numeric(72)
for (i in (62:71)){
    y <- DriverDeaths[1:i, "Deaths"]
    X <- as.matrix(DriverDeaths[1:i, 2:5])
    Population <- DriverDeaths[1:i, "Population"]

    ## Offset included
    glarmamod <- glarma(y, X, offset = log(Population/100000),
                        phiLags = c(12),
                        thetaLags = c(1),
                        type = "Poi", method = "FS",
                        residuals = "Pearson", maxit = 100, grad = 1e-6)
    XT1 <- matrix(allX[i + 1, ], nrow = 1)
    offsetT1 <- log(DriverDeaths$Population[i + 1]/100000)
    mu <- forecast(glarmamod, 1, XT1, offsetT1)$mu
    if (i == 62){
        forecasts[1:62] <- fitted(glarmamod)
    }
    forecasts[i+1] <- mu
}
par(mfrow = c(1,1))
forecasts <- ts(forecasts[63:72], start = c(1985, 10), deltat = 1/12)
fitted <- ts(allFits, start = c(1980, 8), deltat = 1/12)
obs <- ts(DriverDeaths$Deaths, start = c(1980, 8), deltat = 1/12)
plot(obs, ylab = "Driver Deaths", lty = 2,
     main = "Single Vehicle Nighttime Driver Deaths in Utah")
points(obs)
lines(fitted, lwd = 2)
lines(forecasts, col = "red")
par(xpd = NA)
graph.param <-
    legend("top",
           legend = c("observations",expression(estimated~mu[t]),
                      expression(predicted~mu[t])),
           ncol = 3,
           cex = 0.7,
           bty = "n", plot = FALSE)
legend(graph.param$rect$left,
       graph.param$rect$top + graph.param$rect$h,
       legend = c("observations", expression(estimated~mu[t]),
                  expression(predicted~mu[t])),
       col = c("black","black","red"),
       lwd = c(1,2,1), lty = c(2,1,1),
       pch = c(1, NA_integer_, NA_integer_),
       ncol = 3,
       cex = 0.7,
       bty = "n",
       text.font = 4)
par(xpd = FALSE)

### Generate a sample of Y values 2 steps ahead and examine the distribution
data(DriverDeaths)
y <- DriverDeaths[, "Deaths"]
X <- as.matrix(DriverDeaths[, 2:5])
Population <- DriverDeaths[, "Population"]

### Fit the glarma model to the first 70 observations
glarmamod <- glarma(y[1:70], X[1:70, ],
                    offset = log(Population/100000)[1:70],
                    phiLags = c(12),
                    thetaLags = c(1),
                    type = "Poi", method = "FS",
                    residuals = "Pearson", maxit = 100, grad = 1e-6)

nObs <- NROW(X)
n.ahead <- 2
### Specify the X matrix and offset for the times where predictions
### are required
XT1 <- as.matrix(X[(nObs - n.ahead + 1):nObs, ])
offsetT1 <- log(Population/100000)[(nObs - n.ahead + 1):nObs]
nSims <- 500
forecastY <- matrix(ncol = n.ahead, nrow = nSims)
forecastMu <- matrix(ncol = n.ahead, nrow = nSims)

### Generate sample predicted values
for(i in 1:nSims){
    temp <-  forecast(glarmamod, n.ahead, XT1, offsetT1)
    forecastY[i, ] <- temp$Y
    forecastMu[i, ] <- temp$mu
}
### Examine distribution of sample of Y values n.ahead
table(forecastY[, 2])
par(mfrow = c(2,1))
barplot(table(forecastY[, 2]),
        main = "Barplot of Sample Y Values 2 Steps Ahead")
hist(forecastY[, 2], xlab = "Sample Y values",
     breaks=seq(0,max(forecastY[, 2])),
     main = "Histogram of Sample Y Values 2 Steps Ahead\nwith 0.025 and 0.975 Quantiles")
abline(v = quantile(forecastY[, 2], c(0.025, 0.975)), col = "red")

# }