Largely a wrapper for the arima function in the stats package. The main difference is that this function allows a drift term. It is also possible to take an ARIMA model from a previous call to Arima and re-apply it to the data y.

Arima(y, order = c(0, 0, 0), seasonal = c(0, 0, 0), xreg = NULL,
  include.mean = TRUE, include.drift = FALSE, include.constant,
  lambda = model$lambda, biasadj = FALSE, method = c("CSS-ML", "ML",
  "CSS"), model = NULL, x = y, ...)

Arguments

y

a univariate time series of class ts.

order

A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.

seasonal

A specification of the seasonal part of the ARIMA model, plus the period (which defaults to frequency(y)). This should be a list with components order and period, but a specification of just a numeric vector of length 3 will be turned into a suitable list with the specification as the order.

xreg

Optionally, a vector or matrix of external regressors, which must have the same number of rows as y.

include.mean

Should the ARIMA model include a mean term? The default is TRUE for undifferenced series, FALSE for differenced ones (where a mean would not affect the fit nor predictions).

include.drift

Should the ARIMA model include a linear drift term? (i.e., a linear regression with ARIMA errors is fitted.) The default is FALSE.

include.constant

If TRUE, then include.mean is set to be TRUE for undifferenced series and include.drift is set to be TRUE for differenced series. Note that if there is more than one difference taken, no constant is included regardless of the value of this argument. This is deliberate as otherwise quadratic and higher order polynomial trends would be induced.

lambda

Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated.

biasadj

Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.

method

Fitting method: maximum likelihood or minimize conditional sum-of-squares. The default (unless there are missing values) is to use conditional-sum-of-squares to find starting values, then maximum likelihood.

model

Output from a previous call to Arima. If model is passed, this same model is fitted to y without re-estimating any parameters.

x

Deprecated. Included for backwards compatibility.

...

Additional arguments to be passed to arima.

Value

See the arima function in the stats package. The additional objects returned are

x

The time series data

xreg

The regressors used in fitting (when relevant).

Details

See the arima function in the stats package.

See also

auto.arima, forecast.Arima.

Examples

library(ggplot2) WWWusage %>% Arima(order=c(3,1,0)) %>% forecast(h=20) %>% autoplot
# Fit model to first few years of AirPassengers data air.model <- Arima(window(AirPassengers,end=1956+11/12),order=c(0,1,1), seasonal=list(order=c(0,1,1),period=12),lambda=0) plot(forecast(air.model,h=48))
lines(AirPassengers)
# Apply fitted model to later data air.model2 <- Arima(window(AirPassengers,start=1957),model=air.model) # Forecast accuracy measures on the log scale. # in-sample one-step forecasts. accuracy(air.model)
#> ME RMSE MAE MPE MAPE MASE ACF1 #> Training set 0.3576253 7.89734 5.788344 0.1458472 2.670181 0.1982148 0.05807465
# out-of-sample one-step forecasts. accuracy(air.model2)
#> ME RMSE MAE MPE MAPE MASE #> Training set 0.5159268 12.13132 8.14054 0.07949083 1.900931 0.2266508 #> ACF1 #> Training set -0.2166661
# out-of-sample multi-step forecasts accuracy(forecast(air.model,h=48,lambda=NULL), log(window(AirPassengers,start=1957)))
#> ME RMSE MAE MPE MAPE MASE #> Training set 0.35762533 7.8973404 5.78834425 0.1458472 2.670181 0.1982148 #> Test set -0.08403416 0.1031891 0.08801596 -1.3982000 1.463555 0.0030140 #> ACF1 Theil's U #> Training set 0.05807465 NA #> Test set 0.75730561 0.9290965