predict.coco {coconots} | R Documentation |
K-Step Ahead Forecast Distributions
Description
Computes the k-step ahead forecast (distributions) using the models in the coconots package.
Usage
## S3 method for class 'coco'
predict(
object,
k = 1,
number_simulations = 1000,
alpha = 0.05,
simulate_one_step_ahead = FALSE,
max = NULL,
epsilon = 1e-08,
xcast = NULL,
decimals = 4,
julia = FALSE,
...
)
Arguments
object |
An object that has been fitted previously, of class coco. |
k |
The number of steps ahead for which the forecast should be computed (Default: 1). |
number_simulations |
The number of simulation runs to compute (Default: 1000). |
alpha |
Significance level used to construct the prediction intervals (Default: 0.05). |
simulate_one_step_ahead |
If FALSE, the one-step ahead prediction is obtained using the analytical predictive distribution. If TRUE, bootstrapping is used. |
max |
The maximum number of the forecast support for the plot. If NULL all values for which the cumulative distribution function is below 1- epsilon are used for the plot. |
epsilon |
If max is NULL, epsilon determines the range of the support that is used by subsequent automatic plotting using R's plot() function. |
xcast |
An optional matrix of covariate values for the forecasting. If 'NULL', the function assumes no covariates. |
decimals |
Number of decimal places for the forecast probabilities |
julia |
if TRUE, the estimate is predicted with julia (Default: FALSE). |
... |
Optional arguments. |
Details
Returns forecasts for each mass point of the k-step ahead distribution for the fitted model. The exact predictive distributions for one-step ahead predictions for the models included here are provided in Jung and Tremayne (2011), maximum likelihood estimates replace the true model parameters. For k>1 forecast distributions are estimated using a parametric bootstrap. See Jung and Tremanye (2006). Out-of-sample values for covariates can be provided, if necessary.
for k > 1
Value
A list of frequency tables. Each table represents a k-step ahead forecast frequency distribution based on the simulation runs.
References
Jung, R.C. and Tremayne, A. R. (2011) Convolution-closed models for count time series with applications. Journal of Time Series Analysis, 32, 3, 268–280.
Jung, R.C. and Tremayne, A.R. (2006) Coherent forecasting in integer time series models. International Journal of Forecasting 22, 223–238
Examples
length <- 500
pars <- c(1, 0.4)
set.seed(12345)
data <- cocoSim(order = 1, type = "Poisson", par = pars, length = length)
fit <- cocoReg(order = 1, type = "Poisson", data = data)
forecast <- predict(fit, k=1, simulate_one_step_ahead = FALSE)
plot(forecast[[1]]) #plot one-step ahead forecast distribution