# Time Series Forecasting using R programming

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 US consumption and income, use the command: >data (usconsumption) To print the data: >usconsumption`}

Now, form a time series from the data using the command:

{`>ustimeseries <- ts (usconsumption, frequency=4, start=1970) Here, the variable name is “ustimeseries”, frequency is 4(because it is quarterly data), and start=1 (since the data starts from 1970) Now, print the series: >ustimeseries`}

Fit a time series regression model by using the function tslm. Use the command:

{`>fit <- tslm (consumption~income, data=usconsumption) Draw a scatter plot of time series by using the command: >plot (consumption~income, data=usconsumption) Fit the line of best fit for time series by the command: >abline (fit)`}

For scenario based forecasting using R, use the command:

{`> f <- forecast (fit, newdata=data.frame(income=c(-1,1))) To plot the forecasted value, use the following R command to generate the graph of forecasts from linear regression model: > plot (f)`}

## Learn R programming for Statistical Analysis

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