Question

Consider austa , the total international visitors to Australia (in millions) for the period 1980-2015. a. Use auto.arima() to find an appropriate ARIMA model. What model was selected. Check that the residuals look like white noise. Plot forecasts for the next 10 periods. b. Plot forecasts from an ARIMA $(0,1,1)$ model with no drift and compare these to part a. Remove the MA term and plot again. c. Plot forecasts from an ARIMA(2,1,3) model with drift. Remove the constant and see what happens. d. Plot forecasts from an ARIMA $(0,0,1)$ model with a constant. Remove the MA term and plot again. e. Plot forecasts from an ARIMA $(0,2,1)$ model with no constant.

   Consider austa , the total international visitors to Australia (in millions) for the period 1980-2015.
a. Use auto.arima() to find an appropriate ARIMA model. What model was selected. Check that the residuals look like white noise. Plot forecasts for the next 10 periods.
b. Plot forecasts from an ARIMA $(0,1,1)$ model with no drift and compare these to part a. Remove the MA term and plot again.
c. Plot forecasts from an ARIMA(2,1,3) model with drift. Remove the constant and see what happens.
d. Plot forecasts from an ARIMA $(0,0,1)$ model with a constant. Remove the MA term and plot again.
e. Plot forecasts from an ARIMA $(0,2,1)$ model with no constant.

Forecasting: Principles and Practice

George… 2nd Edition

Chapter 8, Problem 8

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Step 1

arima() function, we need to first load the data and convert it into a time series object. Let's assume the data is stored in a vector called "austa_data". ```R library(forecast) # Convert data into a time series object austa_ts <- ts(austa_data, start = 1980, Show more…

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Consider austa , the total international visitors to Australia (in millions) for the period 1980-2015. a. Use auto.arima() to find an appropriate ARIMA model. What model was selected. Check that the residuals look like white noise. Plot forecasts for the next 10 periods. b. Plot forecasts from an ARIMA $(0,1,1)$ model with no drift and compare these to part a. Remove the MA term and plot again. c. Plot forecasts from an ARIMA(2,1,3) model with drift. Remove the constant and see what happens. d. Plot forecasts from an ARIMA $(0,0,1)$ model with a constant. Remove the MA term and plot again. e. Plot forecasts from an ARIMA $(0,2,1)$ model with no constant.

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