← VCE Data Analysis

Data collected over a period of time

Data recorded repeatedly over time, each value with a timestamp, usually at regular intervals, so you can see how values change and spot trends and seasonal patterns.

Part of: VCE Applied Computing - U1O1 - Data analysis — Module 12, ACARA Digital Technologies Years 7–8 — Acquiring and Storing Data — Module 20, ACARA Digital Technologies Years 9–10 — Acquiring and Validating Data — Module 34

Understanding Data collected over a period of time in VCE Data Analysis

What it does: The timestamp (2024-03-01 09:00) is stored beside the value (24.5), so we know when it happened. Summer temperatures are high in January, drop in winter, then return high the next January, showing a repeating seasonal pattern. Time-series data is recorded again and again over time, and each value is linked to a timestamp so you can see how it changes. Regular intervals mean the data is collected at an even, predictable gap, such as every 5 minutes. This makes patterns easier to compare. Daily rainfall is measured repeatedly over a period of time. A one-day survey is a single snapshot, so it does not show change over time.

Common mistakes: Each reading in time-series data needs its own timestamp in order. The date 2024-01-01 is repeated. This row should be 2024-01-02,31. Time-series data is usually collected at regular intervals. These readings are hourly, so the last timestamp should be 11:00, not 11:45.

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This module's key terms are also in a printable crossword (Modules 3–14) and word search (Modules 3–24).

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Learning
Duo
Which best describes time-series data?
Values recorded repeatedly over time, each with a timestampOne value recorded a single time with no date
Matches
Temp: 21, 23, 25, 24, 22
↓
The values have no timestamps, so you cannot tell when each was recorded. It is not time-series data.
Applying
Meanings
Term
time-series data
Meaning
Data recorded repeatedly over time, each value with a timestamp, usually at regular intervals, so you can see how values change and spot trends and seasonal patterns.
Practice
Timestamp,Temp_C
Testing
Gaps
,tempc
Check Answer
Spots
22024-01-01,28
32024-01-01,31

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Quick Questions

1. Which best describes time-series data?
A. One value recorded a single time with no date
B. Values recorded repeatedly over time, each with a timestamp ✓
Time-series data is recorded again and again over time, and each value is linked to a timestamp so you can see how it changes.
2. A school records the temperature at 9:00 am every day for a year. What type of data is this?
A. Time-series data ✓
B. A one-off snapshot
Repeated daily readings, each with a date and time, form a time-series. A snapshot would be a single reading.
3. Why is a timestamp needed in time-series data?
A. It makes each value more accurate
B. It shows when each value was recorded ✓
A timestamp does not change accuracy. It records when the value was collected so values can be put in order and compared over time.
4. Which is an example of a regular collection interval?
A. A sensor logging heart rate every 5 minutes ✓
B. A student checking their phone whenever they feel like it
Regular intervals mean the data is collected at an even, predictable gap, such as every 5 minutes. This makes patterns easier to compare.
5. Monthly sales rise steadily over three years. What is this pattern called?
A. A trend ✓
B. A seasonal pattern
A trend is a general direction over time, such as a steady rise. A seasonal pattern repeats at the same time each cycle, such as each summer.

Match the Pairs

timestamp → The date and time attached to each recorded value so you know when it was collected
interval = 1 hour → A regular collection interval: a new value is recorded every hour
Each reading in time-series data is stored with the time it was recorded → 2024-03-01 09:00, 24.5
A seasonal pattern is a rise or fall that repeats at the same time each year → Jan: 38, Apr: 22, Jul: 12, Oct: 21, Jan: 37
Temp: 21, 23, 25, 24, 22 → The values have no timestamps, so you cannot tell when each was recorded. It is not time-series data.
9:00, 10:00, 10:05, 14:00, 17:30 → The collection intervals are irregular. Readings should be taken at regular times, such as every hour, to compare changes fairly.

Key Term

time-series data
Data recorded repeatedly over time, each value with a timestamp, usually at regular intervals, so you can see how values change and spot trends and seasonal patterns.

Fill in the Blanks

1. timestamp,temp_c 2024-06-01 09:00,14.2 2024-06-01 10:00,15.1
Hint: Type the column name that records when each reading was taken
2. =MAX(B2:B13)
Hint: Type the spreadsheet function that finds the peak (highest) value in the range
3. 2024-06-01 09:00,14.2 2024-06-01 10:00,15.1 2024-06-01 11:00,15.9
Hint: Type the time that keeps the regular one-hour collection interval
4. =MONTH(A2)
Hint: Type the function that pulls the month number out of a date, to group readings by season
5. =B3-B2
Hint: Type the operator that works out the change from one reading to the next, to spot a trend

Spot the Error

Question 1
A Date,Temp_C
B 2024-01-01,28
C 2024-01-01,31
D 2024-01-03,29
Line C — Each reading in time-series data needs its own timestamp in order. The date 2024-01-01 is repeated. This row should be 2024-01-02,31.
Question 2
A Time,Steps
B 09:00,120
C 10:00,95
D 11:45,140
Line D — Time-series data is usually collected at regular intervals. These readings are hourly, so the last timestamp should be 11:00, not 11:45.
Question 3
A Station,Rainfall_mm
B 2024-06-01 09:00,2.4
C 2024-06-01 10:00,0.0
D 2024-06-01 11:00,1.2
Line A — Time-series data needs a timestamp column. The header labels the first column Station, but it holds date-times. It should be Timestamp,Rainfall_mm.
Question 4
A interval <- 60
B IF logging = TRUE
C value <- ReadSensor()
D SAVE value, Now()
E WAIT interval
Line B — An IF runs only once, so the data would not be recorded repeatedly over time. A WHILE loop keeps collecting readings at each interval, so line 2 should be WHILE logging = TRUE.
Question 5
A SET first TO 120, last TO 180
B IF last < first THEN
C OUTPUT 'Trend: rising'
Line B — The values went from 120 up to 180, so the trend is rising. The test should be last > first. With < the program would label a rising trend as falling.

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