# R Programming Tutorial for Forecasting a Linear Trend

To load data set from the package “fma”, use the commands:

{` >data (package=fma) This instantly loads the pre existing data sets in the package. To load the data set regarding tourist travels in Australia over a period of time, use the command: >data (austa) To print the data: >austa `}

*For help on how to load Data in R see this tutorial.*

To fit the time series regression, use the following command in R program:

{`> fit <- tslm (austa~trend) To forecast the values for the next 5 years under 80% and 95 % levels of confidence, use the following R program command: > fcast <- forecast (fit, h=5, level=c(80,95)) Now, plot this forecast using R by the command: > plot (fcast) This will display the forecasts from linear regression model.`}

*See more on learn R for time series analysis tutorial*

To fit the forecast line, use the command:

{`> lines (fitted (fit))`}

To get summary of the regression model in R, use the command:

{`>summary (fit)`}

This will generate the regression model summary of the time series model which contains values of intercept and trend coefficient along with their standard errors and confidence intervals as well as overall F statistics and p value for our time series model.

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