## 2.10 Exercises

Use the help function to explore what the series

`gafa_stock`

,`PBS`

,`vic_elec`

and`pelt`

represent.- Use
`autoplot()`

to plot some of the series in these data sets. - What is the time interval of each series?
- Use
`filter()`

to find what days corresponded to the peak closing price for each of the four stocks in`gafa_stock`

.

- Use
Download the file

`tute1.csv`

from the book website, open it 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.You can read the data into R with the following script:

`readr::read_csv("tute1.csv") tute1 <-View(tute1)`

Convert the data to time series

`tute1 %>% mytimeseries <- mutate(Quarter = yearmonth(Quarter)) %>% as_tsibble(index = Quarter)`

Construct time series plots of each of the three series

`%>% mytimeseries pivot_longer(-Quarter, names_to="Key", values_to="Value") %>% ggplot(aes(x = Quarter, y = Value, colour = Key)) + geom_line() + facet_grid(vars(Key), scales = "free_y")`

Check what happens when you don’t include

`facet_grid()`

.

- Download
`tourism.xlsx`

from the book website and read it into R using`read_excel()`

from the`readxl`

package. - Create a tsibble which is identical to the
`tourism`

tsibble from the`tsibble`

package. - Find what combination of
`Region`

and`Purpose`

had the maximum number of overnight trips on average. - Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.

- Download
Create time plots of the following four time series:

`Bricks`

from`aus_production`

,`Lynx`

from`pelt`

,`Close`

from`gafa_stock`

,`Demand`

from`vic_elec`

.- Use
`?`

(or`help()`

) to find out about the data in each series. - For the last plot, modify the axis labels and title.

- Use
The

`aus_arrivals`

data set comprises quarterly international arrivals to Australia from Japan, New Zealand, UK and the US.- Use
`autoplot()`

,`gg_season()`

and`gg_subseries()`

to compare the differences between the arrivals from these four countries. - Can you identify any unusual observations?

- Use
Monthly Australian retail data is provided in

`aus_retail`

. Select one of the time series as follows (but choose your own seed value):`set.seed(12345678) aus_retail %>% myseries <- filter(`Series ID` == sample(aus_retail$`Series ID`,1))`

Explore your chosen retail time series using the following functions:

`autoplot()`

,`gg_season()`

,`gg_subseries()`

,`gg_lag()`

,`ACF() %>% autoplot()`

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

Use the following graphics functions:

`autoplot()`

,`gg_season()`

,`gg_subseries()`

,`gg_lag()`

,`ACF()`

and explore features from the following time series: Total Private`Employed`

from`us_employment`

, Bricks from`aus_production`

,`Hare`

from`pelt`

,`H02`

cost from`PBS`

, and`us_gasoline`

.- Can you spot any seasonality, cyclicity and trend?
- What do you learn about the series?
- What can you say about the seasonal patterns?
- Can you identify any unusual years?

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.

The

`aus_livestock`

data contains the monthly total number of pigs slaughtered in Victoria, Australia, from Jul 1972 to Dec 2018. Use`filter()`

to extract pig slaughters in Victoria between 1990 and 1995. Use`autoplot`

and`ACF`

for this data and compare these to white noise plots from Figures 2.19 and 2.20.- Use the following code to compute the daily changes in Google closing stock prices.

`gafa_stock %>% dgoog <- filter(Symbol == "GOOG", year(Date) >= 2018) %>% mutate(trading_day = row_number()) %>% update_tsibble(index = trading_day, regular = TRUE) %>% mutate(diff = difference(Close))`

- Why was it necessary to re-index the tsibble?
- Plot these differences and their ACF.
- Do the changes in the stock prices look like white noise?