UComp (version 5.0.4)

ARIMAmodel: ARIMAmodel

Description

Estimates and forecasts ARIMA general univariate models

Usage

ARIMAmodel(
  y,
  u = NULL,
  model = NULL,
  cnst = NULL,
  s = frequency(y),
  criterion = "bic",
  h = 2 * s,
  verbose = FALSE,
  lambda = 1,
  maxOrders = c(3, 2, 3, 2, 1, 2),
  bootstrap = FALSE,
  nSimul = 5000,
  fast = FALSE
)

Value

An object of class ARIMA. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ARIMA object as specified in what follows (function

ARIMA fills in all of them at once):

After running ARIMAmodel or ARIMA:

p

Estimated parameters

yFor

Forecasted values of output

yForV

Variance of forecasted values of output

ySimul

Bootstrap simulations for forecasting distribution evaluation

After running ARIMAvalidate:

table

Estimation and validation table

Arguments

y

a time series to forecast (it may be either a numerical vector or a time series object). This is the only input required. If a vector, the additional input s should be supplied compulsorily (see below).

u

a matrix of input time series. If the output wanted to be forecast, matrix u should contain future values for inputs.

model

the model to estimate. A vector c(p,d,q,P,D,Q) containing the model orders of an ARIMA(p,d,q)x(P,D,Q)_s model. A constant may be estimated with the cnst input. Use a NULL to automatically identify the ARIMA model.

cnst

flag to include a constant in the model (TRUE/FALSE/NULL). Use NULL to estimate

s

seasonal period of time series (1 for annual, 4 for quarterly, ...)

criterion

information criterion for identification stage ("aic", "bic", "aicc")

h

forecast horizon. If the model includes inputs h is not used, the lenght of u is used instead.

verbose

intermediate estimation output (TRUE / FALSE)

lambda

Box-Cox lambda parameter (NULL: estimate)

maxOrders

a vector c(p,d,q,P,D,Q) containing the maximum orders of model orders to search for in the automatic identification

bootstrap

use bootstrap simulation for predictive distributions

nSimul

number of simulation runs for bootstrap simulation of predictive distributions

fast

fast identification (avoids post-identification checks)

Author

Diego J. Pedregal

Details

ARIMAmodel is a function for modelling and forecasting univariate time series with Autoregressive Integrated Moving Average (ARIMA) time series models. It sets up the model with a number of control variables that govern the way the rest of functions in the package will work. It also estimates the model parameters by Maximum Likelihood and forecasts the data.

See Also

ARIMA, ARIMAvalidate,

Examples

if (FALSE) {
y <- log(AirPAssengers)
m1 <- ARIMAmodel(y)
m1 <- ARIMAmodel(y, lambda = NULL)
}