2.10 Exercises

  1. Use the help menu to explore what the series gold, woolyrnq and gas represent. These are available in the forecast package.

    1. Use autoplot to plot each of these in separate plots.

    2. What is the frequency of each commodity series? Hint: apply the frequency() function.

    3. Use which.max() to spot the outlier in the gold series. Which observation was it?

  2. Download some data from OTexts.org/fpp2/extrafiles/tute1.csv. Open the file tute1.csv in Excel (or some other spreadsheet application) and review its contents. You should find four columns of information. Columns B through D each contain a quarterly series, labelled Sales, AdBudget and GDP. Sales contains the quarterly sales for a small company over the period 1981-2005. AdBudget is the advertising budget and GDP is the gross domestic product. All series have been adjusted for inflation.

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

      tute1 <- read.csv("tute1.csv", header=TRUE)
    2. Convert the data to time series

      mytimeseries <- ts(tute1[,-1], start=1981, frequency=4)

      (The [,-1] removes the first column which contains the quarters as we don’t need them now.)

    3. Construct time series plots of each of the three series

      autoplot(mytimeseries, facets=TRUE)

      Check what happens when you don’t include facets=TRUE.

  3. 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):

      myts <- 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?

  4. Repeat for the following series:

    bicoal, chicken, dole, usdeaths, bricksq, lynx, ibmclose, sunspotarea, hsales, hyndsight and gasoline.

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

  5. 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?

  6. 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.

  7. The pigs data shows the monthly total number of pigs slaughtered in Victoria, Australia, from Jan 1980 to Aug 1995. Use mypigs <- window(pigs, start=1990) to select the data starting from 1990. Use autoplot and ggAcf for mypigs series and compare these to white noise plots from Figures 2.13 and 2.14.