Calculating Cumulative Average for the Latest Entries

When dealing with data that has multiple entries per date and per id, calculating cumulative averages can be a challenging task. In this article, we will explore how to calculate the cumulative average of values over ids for each date, taking into account only the last few entries.

Understanding the Problem

Suppose we have a table with columns id, value, y, m, and d. The first two columns represent the row number within the date and the date itself in the format YYYYMMDD. We want to calculate the cumulative average for each date, where only the last few entries are considered.

For example, let’s consider a table with the following data:

We want to produce the following output:

Initial Attempt

The initial attempt involves using a combination of ROW_NUMBER(), LAG(), and window functions to get the cumulative sum.

SELECT date_parse(cast(c.y*10000+c.m*100+c.d as varchar), '%Y%m%d') as date, avg(s.value) as cum_aver 
FROM (
  SELECT id, value, date_parse(cast(y*10000+m*100+d as varchar), '%Y%m%d') as date,
         ROW_NUMBER () OVER (PARTITION BY id ORDER BY date_parse (cast(y*10000+m*100+d as varchar), '%Y%mhd') DESC, id DESC) rn
  FROM table
) s 
JOIN table c ON 
  s.date <= date_parse(cast(c.y*10000+c.m*100+c.d as varchar), '%Y%m%d')
GROUP BY c.y, c.m, c.d;

However, this query does not produce the desired output. We need to rethink our approach.

Alternative Approach

To calculate the cumulative average of values over ids for each date, we can use a combination of window functions and grouping.

The idea is to take the most recent value for each id and divide by the number of different ids. To get the sum, one method is to keep the first value and then take successive differences. The sum of these differences is the sum at any point in time. The number of different ids – well, just count the first one you see.

Here’s how we can implement this using SQL:

SELECT y, m, d,
       (sum(sum(value - prev_value)) over (order by y, m, d) / 
        sum(sum(case when seqnum = 1 then 1 else 0 end)) over (order by y, m, d)
       ) as average
FROM (
  SELECT t.*,
         row_number() over (partition by id order by y, m, d) as seqnum,
         lag(value, 1, 0) over (partition by id order by y, m, d) as prev_value
  FROM table t
) t
GROUP BY y, m, d;

This query uses a subquery to assign a row number (seqnum) to each row within the date and then calculates the cumulative sum of differences between consecutive values. The case expression is used to count the number of different ids.

Conclusion

Calculating cumulative averages for the latest entries can be challenging, especially when dealing with data that has multiple entries per date and per id. By using a combination of window functions and grouping, we can effectively calculate the desired output. This approach may require some creative thinking, but it provides a robust solution to common problems in data analysis.

Example Use Cases

This technique is particularly useful in situations where:

• You need to calculate cumulative averages over time-based data.
• Your data has multiple entries per date and per id.
• You want to consider only the last few entries when calculating cumulative averages.

Step-by-Step Solution

Here’s a step-by-step guide on how to implement this solution:

1. Identify the Problem: Determine why you need to calculate cumulative averages for the latest entries in your data.
2. Understand the Data Structure: Familiarize yourself with the structure of your data, including any date and time columns.
3. Choose a Solution Approach: Select an approach from the two methods discussed: the initial attempt using ROW_NUMBER() and LAG(), or the alternative approach using window functions and grouping.
4. Implement the Solution: Write SQL code to implement the chosen solution.
5. Test and Refine: Test your query with sample data and refine it as needed to ensure accurate results.

By following these steps, you can effectively calculate cumulative averages for the latest entries in your data and make informed decisions based on your analysis.

Last modified on 2025-01-20