## 2.10 Exercises

1. Download some monthly Australian retail data from http://robjhyndman.com/data/retail.xlsx. These represent retail sales in various categories for different Australian states, and are stored in a MS-Excel file.

1. You can read the data into R with the following script:

retaildata <- readxl::read_excel("retail.xlsx", skip = 1)

You may need to first install the readxl package.

2. Select one of the time series as follows (but replace the column name with your own chosen column):

mytimeseries <- ts(retaildata[,"A3349873A"], frequency=12, start=c(1982,4))
3. Explore your chosen retail time series using the following functions:

autoplot, ggseasonplot, ggsubseriesplot, gglagplot, ggAcf

Can you spot any seasonality, cyclicity and trend? What do you learn about the series?

2. Repeat for the following series:

bicoal, chicken, dole, usdeaths, bricksq, lynx, ibmclose

Use the help files to find out what the series are.

3. The arrivals data set comprises quarterly international arrivals (in thousands) to Australia from Japan, New Zealand, UK and the US. Use autoplot and ggseasonplot and compare the differences between the arrivals from these four countries. Can you identify any unusual observations?

4. The following time plots and ACF plots correspond to four different time series. Your task is to match each time plot in the first row with one of the ACF plots in the second row.