coconots (version 2.0.1)

predict.coco: K-Step Ahead Forecast Distributions

Description

Computes the k-step ahead forecast (distributions) using the models in the coconots package.

Usage

# S3 method for 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,
  ...
)

Value

A list of frequency tables. Each table represents a k-step ahead forecast frequency distribution based on the simulation runs.

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

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