R: Forecasting time series

forecast {forecast}

R Documentation

Forecasting time series

Description

forecast is a generic function for forecasting from time series or time series models. The function invokes particular methods which depend on the class of the first argument.

For example, the function forecast.Arima makes forecasts based on the results produced by arima.

The function forecast.ts makes forecasts using ets models (if the data are non-seasonal or the seasonal period is 12 or less) or stlf (if the seasonal period is 13 or more).

Usage

forecast(object,...)
## S3 method for class 'ts'
forecast(object, h, level=c(80,95), fan=FALSE, ...)

Arguments

`object`	a time series or time series model for which forecasts are required
`h`	Number of periods for forecasting
`level`	Confidence level for prediction intervals.
`fan`	If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
`...`	Additional arguments affecting the forecasts produced. `forecast.ts` passes these to `forecast.ets` or `stlf` depending on the frequency of the time series.

Value

An object of class "forecast".

The function summary is used to obtain and print a summary of the results, while the function plot produces a plot of the forecasts and prediction intervals.

The generic accessor functions fitted.values and residuals extract various useful features of the value returned by forecast$model.

An object of class "forecast" is a list containing at least the following elements:

`model`	A list containing information about the fitted model
`method`	The name of the forecasting method as a character string
`mean`	Point forecasts as a time series
`lower`	Lower limits for prediction intervals
`upper`	Upper limits for prediction intervals
`level`	The confidence values associated with the prediction intervals
`x`	The original time series (either `object` itself or the time series used to create the model stored as `object`).
`residuals`	Residuals from the fitted model. That is x minus fitted values.
`fitted`	Fitted values (one-step forecasts)

Author(s)