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.
# A tibble: 24,320 × 5
Quarter Region State Purpose Trips
<chr> <chr> <chr> <chr> <dbl>
1 1998-01-01 Adelaide South Australia Business 135.
2 1998-04-01 Adelaide South Australia Business 110.
3 1998-07-01 Adelaide South Australia Business 166.
4 1998-10-01 Adelaide South Australia Business 127.
5 1999-01-01 Adelaide South Australia Business 137.
6 1999-04-01 Adelaide South Australia Business 200.
7 1999-07-01 Adelaide South Australia Business 169.
8 1999-10-01 Adelaide South Australia Business 134.
9 2000-01-01 Adelaide South Australia Business 154.
10 2000-04-01 Adelaide South Australia Business 169.
# ℹ 24,310 more rows
# 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
#checking if it is identical 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
Observation: Trend: There is a strong increasing trend in private employment over time. More recent years have much higher employment levels than earlier years. Cause could be internet, technologies, immigration. Cyclicity: There are periods where employment decreases for some time before recovering and continuing its long-term growth, suggesting cyclical movements, the autoplot is better to visualize that cyclicity pattern.
Private employment has a strong upward trend over time, although there are some periods of decline and recovery. This due to school ending, summer jobs, entry - level jobs and also holidays part time jobs.
Employment generally increases from January to June, remains relatively stable during the summer, decreases slightly around September, and then slightly increases or remains stable toward the end of the year. This pattern repeats across many years.
Some years show larger decreases than the usual seasonal pattern. The time-series plot shows particularly noticeable downturns around 2008–2009 and 2020.
Warning: The `...` argument of `PACF()` is deprecated as of feasts 0.2.2.
ℹ ACF variables should be passed to the `y` argument. If multiple variables are
to be used, specify them using `vars(...)`.
Warning: ACF currently only supports one column, `Bricks` will be used.
Observation: Trend: There is a strong increasing trend on aus_productions ‘Bricks’ over time. More recent years have much higher Bricks production than earlier years. Cause could be population growth, immigration, housing, tourism. Cyclicity: There are periods where Bricks production decreases for some time before recovering and continuing its long-term growth, suggesting cyclical movements, the autoplot is better to visualize that cyclicity pattern.
Bricks production has a strong upward trend over time, although there are some periods of decline and recovery like around years 1980-1984 which we can observe a peak. the same pattern repeat over the time.
Bricks production generally increases from Q1 to Q2, remains relatively stable from Q2 to Q3 then decreases slightly from Q3 to Q4.
1975 to 1985 are the years we see increase on Bricks production in Australia, then down during early 2000’s to 2005.
Warning: ACF currently only supports one column, `Hare` will be used.
Observation: Trend: There is a strong increasing and decrese trend in Hares population over time. Cyclicity: The pattern tend to repeat throughout the years, increase - decrease and not stable.
The series show how the Hares population are affected from 1800 to 1920 with a pick in 1865 right followed with a strong decrease and pattern is repeated.
For this Hare data we could not use a seasonal plot because it is reported yearly.
Some cycles have much larger peaks than others, particularly around the 1860s and 1880s. noticeable downturns around 1863, 1868– 1900 and 1920 also a upturn increase in years 1865 and 1885 .
Observation: Trend: There is a strong decrease from January to February, then slightly upward increase from February through December trend in Cost from PBS over time. The same pattern repeat throughout the years 1995 to 2005. Seasonality: There are periods where Cost decreases for some time before recovering and continuing its long-term growth, suggesting cyclical movements, the seasonal plot is better to visualize that cyclicity pattern.
Cost has a strong upward trend over time, although there are some periods of decline and recovery. This due to new year and holidays.
Cost generally increases relatively from February through December. This pattern repeats across many years.
unusual years I can identify is year 2005 where we can observe a decrease from February through March
us_gasoline |>gg_season(Barrels)
us_gasoline |>gg_subseries(Barrels)
us_gasoline |>gg_lag(Barrels, geom ="point")
us_gasoline |>autoplot(Barrels)
us_gasoline |>ACF(Barrels) |>autoplot()
Observation: Trend: There is a strong increasing trend in USA Barrels over time. More recent years have much higher number of Barrels than earlier years. Cyclicity: There are periods where the number decreases for some time before recovering vice versa, suggesting cyclical movements, the autoplot is better to visualize that cyclicity pattern.
Barrels number has a strong upward trend over time, although there are some periods of decline and recovery. This due to the USA importing Barrels and competition.
The number of Barrels has increase over time, observing the seasonal pattern we see an up and down.
The trends are almost the same through years 1995 to 2015 almost the same pattern occur every year.
