# A tibble: 66,037 × 5
Sensor Date_Time Date Time Count
<chr> <dttm> <date> <int> <int>
1 Birrarung Marr 2015-01-01 00:00:00 2015-01-01 0 1630
2 Birrarung Marr 2015-01-01 01:00:00 2015-01-01 1 826
3 Birrarung Marr 2015-01-01 02:00:00 2015-01-01 2 567
4 Birrarung Marr 2015-01-01 03:00:00 2015-01-01 3 264
5 Birrarung Marr 2015-01-01 04:00:00 2015-01-01 4 139
6 Birrarung Marr 2015-01-01 05:00:00 2015-01-01 5 77
7 Birrarung Marr 2015-01-01 06:00:00 2015-01-01 6 44
8 Birrarung Marr 2015-01-01 07:00:00 2015-01-01 7 56
9 Birrarung Marr 2015-01-01 08:00:00 2015-01-01 8 113
10 Birrarung Marr 2015-01-01 09:00:00 2015-01-01 9 166
# ℹ 66,027 more rows
Activities: Week 1
Time series data and patterns
Exercise 1
The pedestrian
dataset contains hourly pedestrian counts from 2015-01-01 to 2016-12-31 at 4 sensors in the city of Melbourne.
The data is shown below:
Identify the index
variable, key
variable(s), and measured variable(s) of this dataset.
- The
index
variable contains the complete time information - The
key
variable(s) identify each time series - The measured variable(s) are what you want to explore/forecast.
index
variable
key
variable(s)
measured variable(s)
Exercise 2
The aus_accommodation
dataset contains quarterly data on Australian tourist accommodation from short-term non-residential accommodation with 15 or more rooms, 1998 Q1 - 2016 Q2.
The units of the measured variables are as follows:
- Takings are in millions of Australian dollars
- Occupancy is a percentage of rooms occupied
- CPI is an index with value 100 in 2012 Q1.
Complete the code to convert this dataset into a tsibble.
Exercise 3
The previous exercise produced a dataset with daily frequency - although clearly the data is quarterly! This is because we are using a daily granularity which is inappropriate for this data.
Common temporal granularities can be created with these functions:
Granularity | Function |
---|---|
Annual | as.integer() |
Quarterly | yearquarter() |
Monthly | yearmonth() |
Weekly | yearweek() |
Daily | as_date() , ymd() |
Sub-daily | as_datetime() |
Use the appropriate granularity for the aus_accommodation
dataset, and verify that the frequency is now quarterly.
Exercise 4
The tourism
dataset contains the quarterly overnight trips from 1998 Q1 to 2016 Q4 across Australia.
It is disaggregated by 3 key variables:
State
: States and territories of AustraliaRegion
: The tourism regions are formed through the aggregation of Statistical Local Areas (SLAs) which are defined by the various State and Territory tourism authorities according to their research and marketing needsPurpose
: Stopover purpose of visit: “Holiday”, “Visiting friends and relatives”, “Business”, “Other reason”.
Calculate the total quarterly tourists visiting Victoria from the tourism
dataset.
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.