Understanding the Problem and its Requirements

The problem at hand involves analyzing a dataset collected every minute, which includes temperature readings. The goal is to calculate the aggregate average result of temperature for each range of minutes throughout the day (0-1439). This requires aggregating data by hour and minute, rather than just day or hour.

The Current Data Collection Approach

The current approach involves collecting data in a specific format every minute, which includes an id (auto-incrementing), a timestamp (ts) in *nix format, and the temperature reading (temp). Two SQL queries are provided:

# First query: Average temperature by day
select 
    day(ts) as day,
    avg(temp) as avg_1 from scores group by day(ts) order by day(ts) asc

# Second query: Average temperature by hour
select 
    hour(ts) as hour,
    avg(temp) as avg_1 from scores group by hour(ts) order by hour(ts) asc

These queries provide the average temperature for each day and hour, respectively.

The Solution Approach

The solution involves calculating the aggregate average result of temperature for each minute of the day. To achieve this, we need to:

• Extract the hour and minute from the timestamp.
• Group the data by these two values.
• Calculate the average temperature for each group.

Step 1: Extracting Hour and Minute from Timestamp

We can extract the hour and minute from the timestamp using PostgreSQL’s built-in functions EXTRACT(HOUR FROM ts) and EXTRACT(MINUTE FROM ts), respectively.

# Query to extract hour and minute from timestamp
select 
    EXTRACT(HOUR FROM ts) as hour_,
    EXTRACT(MINUTE FROM ts) as minute_
from scores

Step 2: Grouping Data by Hour and Minute

We group the data by these two values using the GROUP BY clause.

# Query to group data by hour and minute
select 
    EXTRACT(HOUR FROM ts) as hour_,
    EXTRACT(MINUTE FROM ts) as minute_,
    avg(temp) as temp_
from scores
group by EXTRACT(HOUR FROM ts), EXTRACT(MINUTE FROM ts)

Step 3: Calculating Average Temperature for Each Group

We calculate the average temperature for each group using the avg function.

# Query to calculate average temperature for each group
select 
    (A.hour_ * 60) + A.minute_ as time_,
    A.*
from (
    select 
       EXTRACT(HOUR FROM ts) as hour_, 
       EXTRACT(MINUTE FROM ts) as minute_, 
       avg(temp) as temp_
      from scores
        group by EXTRACT(HOUR FROM ts), EXTRACT(MINUTE FROM ts)
) A

Step 4: Representing Time in Minutes

To represent the time in minutes, we multiply the hour by 60 and add the minute.

# Query to represent time in minutes
select 
    (A.hour_ * 60) + A.minute_ as time_,
    A.*
from (
    select 
       EXTRACT(HOUR FROM ts) as hour_, 
       EXTRACT(MINUTE FROM ts) as minute_, 
       avg(temp) as temp_
      from scores
        group by EXTRACT(HOUR FROM ts), EXTRACT(MINUTE FROM ts)
) A

Conclusion

To calculate the aggregate average result of temperature for each range of minutes throughout the day, we need to extract the hour and minute from the timestamp, group the data by these two values, and calculate the average temperature for each group. This approach allows us to analyze the temperature on each pair of hour/minute of all days.

Example Use Case

Suppose we have a table scores with the following data:

Running the following query:

# Query to calculate average temperature for each minute
select 
    (A.hour_ * 60) + A.minute_ as time_,
    A.*
from (
    select 
       EXTRACT(HOUR FROM ts) as hour_, 
       EXTRACT(MINUTE FROM ts) as minute_, 
       avg(temp) as temp_
      from scores
        group by EXTRACT(HOUR FROM ts), EXTRACT(MINUTE FROM ts)
) A

Will produce the following result:

This result shows the average temperature for each minute of the hour (in this case, 12:00) on a specific day.

Additional Considerations

When working with time-series data, it’s essential to consider additional factors that may impact your analysis, such as:

• Handling missing values
• Accounting for seasonal variations or trends
• Incorporating external factors, like weather patterns or geographical location

By understanding the nuances of time-series data and applying appropriate analytical techniques, you can gain valuable insights into complex phenomena.

Last modified on 2023-09-19