ARIMAforecast function

ARIMAforecast

Estimates and forecasts ARIMA general univariate models

ARIMAforecast(
  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
)

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)

Returns

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 ARIMAforecast 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

Details

ARIMAforecast 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.

Examples

## Not run:

y <- log(AirPAssengers)
m1 <- ARIMAforecast(y)
m1 <- ARIMAforecast(y, lambda = NULL)
## End(Not run)

See Also

ARIMA, ARIMAvalidate,

Author(s)

Diego J. Pedregal