forecast: acp Forecast

In mpiktas/acp: Autoregressive Conditional Poisson

Description

Recursive forecasting of an acp model.

Usage

forecast(object, sample, ydata,...)

Arguments

object	an object of class "acp", i.e., a fitted model.
sample	the point of sample from which the recursive forecast process will begin.
ydata	a data frame containing the real values of the dependent varible.
...	not used.

Value

a series of forecasted values

Author(s)

Siakoulis Vasileios

Examples

data(polio)

#Create time trend and seasonality variables
trend=(1:168/168)
cos12=cos((2*pi*(1:168))/12)
sin12=sin((2*pi*(1:168))/12)
cos6=cos((2*pi*(1:168))/6)
sin6=sin((2*pi*(1:168))/6)
polio_data<-data.frame(polio, trend , cos12, sin12, cos6, sin6)

mod1 <- acp(polio~-1+trend+cos12+sin12+cos6+sin6,data=polio_data)
fitfor<-forecast(mod1,158,polio_data[[1]])

mpiktas/acp documentation built on May 19, 2019, 11:40 a.m.