# forecast: Forecast future values In itsmr: Time Series Analysis Using the Innovations Algorithm

## Description

Forecast future values

## Usage

 `1` ```forecast(x, M, a, h = 10, opt = 2, alpha = 0.05) ```

## Arguments

 `x` Time series data `M` Data model `a` ARMA model `h` Steps ahead `opt` Display option (0 silent, 1 tabulate, 2 plot and tabulate) `alpha` Level of significance

## Details

The data model can be `NULL` for none. Otherwise `M` is a vector of function names and arguments.

Example:

`M = c("log","season",12,"trend",1)`

The above model takes the log of the data, then subtracts a seasonal component of period 12, then subtracts a linear trend component.

These are the available functions:

 `diff` Difference the data. Has a single argument, the lag. `hr` Subtract harmonic components. Has one or more arguments, each specifying the number of observations per harmonic. `log` Take the log of the data, has no arguments. `season` Subtract a seasonal component. Has a single argument, the number of observations per season. `trend` Subtract a trend component. Has a single argument, the order of the trend (1 linear, 2 quadratic, etc.)

At the end of the model there is an implicit subtraction of the mean operation. Hence the resulting time series always has zero mean.

All of the functions are inverted before the forecast results are displayed.

## Value

Returns the following list invisibly.

 `pred` Predicted values `se` Standard errors (not returned for data models with log) `l` Lower bounds (95% confidence interval) `u` Upper bounds

`arma` `Resid` `test`

## Examples

 ```1 2 3 4``` ```M = c("log","season",12,"trend",1) e = Resid(wine,M) a = arma(e,1,1) forecast(wine,M,a) ```

itsmr documentation built on Sept. 11, 2018, 1:05 a.m.