Introduction

This assignment explores different time series datasets from the fpp3 package and related sources.
I used simple R code with explanations to answer each question.
tute1 is read directly from the book’s website to avoid missing file issues.

Question 1: Explore Four Time Series

We look at Bricks (aus_production), Lynx (pelt), Close (gafa_stock), and Demand (vic_elec).

# Extract series
bricks <- aus_production |> select(Quarter, Bricks)
lynx   <- pelt |> select(Year, Lynx)
gafa   <- gafa_stock |> select(Symbol, Date, Close)
demand <- vic_elec |> select(Time, Demand)

# Time intervals
interval(bricks)
## <interval[1]>
## [1] 1Q
interval(lynx)
## <interval[1]>
## [1] 1Y
interval(gafa)
## <interval[1]>
## [1] !
interval(demand)
## <interval[1]>
## [1] 30m
# Plots
autoplot(bricks)
## Plot variable not specified, automatically selected `.vars = Bricks`
## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_line()`).

autoplot(lynx)
## Plot variable not specified, automatically selected `.vars = Lynx`

autoplot(gafa, Close)

autoplot(demand) +
  labs(title = "Electricity Demand in Victoria",
       x = "Time", y = "Demand (MW)")
## Plot variable not specified, automatically selected `.vars = Demand`

Question 2: Peak Closing Price in GAFA Stocks

gafa_stock |>
  group_by(Symbol) |>
  filter(Close == max(Close)) |>
  select(Symbol, Date, Close)
## # A tsibble: 4 x 3 [!]
## # Key:       Symbol [4]
## # Groups:    Symbol [4]
##   Symbol Date       Close
##   <chr>  <date>     <dbl>
## 1 AAPL   2018-10-03  232.
## 2 AMZN   2018-09-04 2040.
## 3 FB     2018-07-25  218.
## 4 GOOG   2018-07-26 1268.

Question 3: Tute1 Dataset (read from web)

tute1 is not bundled in fpp3, so we load it directly from the book website.

tute1 <- read_csv("https://otexts.com/fpp3/extrafiles/tute1.csv")
## Rows: 100 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl  (3): Sales, AdBudget, GDP
## date (1): Quarter
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
mytimeseries <- tute1 |>
  mutate(Quarter = yearquarter(Quarter)) |>
  as_tsibble(index = Quarter)

# Plot with facets
mytimeseries |>
  pivot_longer(-Quarter) |>
  ggplot(aes(x = Quarter, y = value, colour = name)) +
  geom_line() +
  facet_grid(name ~ ., scales = "free_y")

# Without facets
mytimeseries |>
  pivot_longer(-Quarter) |>
  ggplot(aes(x = Quarter, y = value, colour = name)) +
  geom_line()

Question 4: US Gas Consumption (New England)

usgas <- us_total |> as_tsibble(index = year, key = state)

usgas |>
  filter(state %in% c("Maine","Vermont","New Hampshire",
                      "Massachusetts","Connecticut","Rhode Island")) |>
  autoplot(y) +
  labs(title="US Natural Gas Consumption - New England")

Question 5: Tourism Data (from fpp3)

tourism
## # A tsibble: 24,320 x 5 [1Q]
## # Key:       Region, State, Purpose [304]
##    Quarter Region   State           Purpose  Trips
##      <qtr> <chr>    <chr>           <chr>    <dbl>
##  1 1998 Q1 Adelaide South Australia Business  135.
##  2 1998 Q2 Adelaide South Australia Business  110.
##  3 1998 Q3 Adelaide South Australia Business  166.
##  4 1998 Q4 Adelaide South Australia Business  127.
##  5 1999 Q1 Adelaide South Australia Business  137.
##  6 1999 Q2 Adelaide South Australia Business  200.
##  7 1999 Q3 Adelaide South Australia Business  169.
##  8 1999 Q4 Adelaide South Australia Business  134.
##  9 2000 Q1 Adelaide South Australia Business  154.
## 10 2000 Q2 Adelaide South Australia Business  169.
## # ℹ 24,310 more rows
# Find combination with max average trips
tourism |>
  group_by(Region, Purpose) |>
  summarise(avg_trips = mean(Trips, na.rm=TRUE)) |>
  arrange(desc(avg_trips))
## Warning: Current temporal ordering may yield unexpected results.
## ℹ Suggest to sort by `Region`, `Purpose`, `Quarter` first.
## # A tsibble: 24,320 x 4 [1Q]
## # Key:       Region, Purpose [304]
## # Groups:    Region [76]
##    Region          Purpose  Quarter avg_trips
##    <chr>           <chr>      <qtr>     <dbl>
##  1 Melbourne       Visiting 2017 Q4      985.
##  2 Sydney          Business 2001 Q4      948.
##  3 Sydney          Visiting 2016 Q4      921.
##  4 Sydney          Visiting 2017 Q4      920.
##  5 Sydney          Visiting 2017 Q1      916.
##  6 South Coast     Holiday  1998 Q1      915.
##  7 North Coast NSW Holiday  2016 Q1      906.
##  8 Sydney          Business 2017 Q3      892.
##  9 Sydney          Business 2017 Q2      884.
## 10 Sydney          Visiting 2013 Q4      882.
## # ℹ 24,310 more rows
# Aggregate by State (fixed version using index_by)
tourism |>
  index_by(Quarter) |>
  group_by(State) |>
  summarise(Trips = sum(Trips))
## # A tsibble: 640 x 3 [1Q]
## # Key:       State [8]
##    State Quarter Trips
##    <chr>   <qtr> <dbl>
##  1 ACT   1998 Q1  551.
##  2 ACT   1998 Q2  416.
##  3 ACT   1998 Q3  436.
##  4 ACT   1998 Q4  450.
##  5 ACT   1999 Q1  379.
##  6 ACT   1999 Q2  558.
##  7 ACT   1999 Q3  449.
##  8 ACT   1999 Q4  595.
##  9 ACT   2000 Q1  600.
## 10 ACT   2000 Q2  557.
## # ℹ 630 more rows

Question 6: Aus Arrivals

aus_arrivals |>
  autoplot(Arrivals) +
  labs(title="International Arrivals to Australia")

aus_arrivals |> gg_season(Arrivals)
## Warning: `gg_season()` was deprecated in feasts 0.4.2.
## ℹ Please use `ggtime::gg_season()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

aus_arrivals |> gg_subseries(Arrivals)
## Warning: `gg_subseries()` was deprecated in feasts 0.4.2.
## ℹ Please use `ggtime::gg_subseries()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Question 7: Retail Series

set.seed(12345678)
myseries <- aus_retail |>
  filter(`Series ID` == sample(aus_retail$`Series ID`, 1))

autoplot(myseries, Turnover)

gg_season(myseries, Turnover)

gg_subseries(myseries, Turnover)

gg_lag(myseries, Turnover)
## Warning: `gg_lag()` was deprecated in feasts 0.4.2.
## ℹ Please use `ggtime::gg_lag()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

ACF(myseries, Turnover) |> autoplot()

Question 8: Other Series

# US Employment – Total Private
us_employment |>
  filter(Title=="Total Private") |>
  autoplot(Employed)

# Bricks
autoplot(aus_production, Bricks)
## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_line()`).

# Hare
autoplot(pelt, Hare)

# PBS H02 Cost
PBS |>
  filter(ATC2=="H02") |>
  autoplot(Cost)

# US Gasoline
autoplot(us_gasoline, Barrels)

Question 9: Pig Slaughters in Victoria (1990–1995)

vic_pigs <- aus_livestock |>
  filter(Animal=="Pigs", State=="Victoria",
         between(Month, yearmonth("1990 Jan"), yearmonth("1995 Dec")))

autoplot(vic_pigs, Count)

ACF(vic_pigs, Count) |> autoplot()

Question 10: Google Stock Daily Changes

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

autoplot(dgoog, diff)
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).

ACF(dgoog, diff) |> autoplot()

Conclusion

This assignment explored multiple datasets, time intervals, and visualization techniques using fpp3.
I practiced using autoplot, gg_season, gg_subseries, gg_lag, and ACF to study seasonality, cyclicity, and unusual patterns in time series.