Filling Null Values in Time Series Data for Weekends with Previous Day Values

In this article, we will explore a common problem that arises when working with time series data: filling null values for weekends. Specifically, we will focus on Saturdays and Sundays, where the data is typically missing due to the weekend closure of financial markets.

Problem Statement

Suppose you have a view with dates, stock names, and daily stock prices for weekdays. The data excludes Saturday and Sunday entries. Your goal is to fill in the missing values for these weekends with the corresponding stock names and previous day’s prices.

Background Information

To understand this problem better, let’s first analyze the data:

As shown in the table, we have missing values for Saturdays (June 30th) and Sundays (June 30th). We want to fill these gaps with the corresponding stock names and previous day’s prices.

SQL Query

To solve this problem, we will use a combination of cross join, left join, and lag() functions in SQL. The query is as follows:

select d.date, s.stockname,
       coalesce(t.price,
                lag(t.price ignore nulls) over (partition by s.stockname order by d.date)
               ) as price
from (select distinct date from t
     ) d cross join
     (select stockname from t where stockname is not null
     ) s left join
     t
     on t.date = d.date and t.stockname = s.stockname
order by d.date desc, s.stockname;

How the Query Works

Let’s break down the query step by step:

1. Cross Join: The subquery (select distinct date from t) generates a list of unique dates in our dataset. We then perform a cross join with another subquery (select stockname from t where stockname is not null). This results in each date being paired with every unique stock name that has data.

2. Left Join: The left join operation combines the result of the cross join with our original dataset (t). We only include rows where the date exists in t, effectively filtering out Saturdays and Sundays since their values are missing.

3. LAG() Function: To fill in the null values for weekends, we use the lag() function. This function returns the value of a column before a specific row. In our case, we use it to get the previous day’s price (if available).

   • We use the ignore nulls option within lag(). Not all databases support this functionality, but it helps avoid errors when dealing with missing values.

• Coalesce: The coalesce() function returns the first non-null value from a list of arguments. In our case, we pass in two expressions: the price and the result of the lag() function. This ensures that if there is no previous day’s price (i.e., it’s the first row), the price will be null; otherwise, it will use the previous day’s price.

Example Output

The expected output for our sample data would look like this:

The final result has all the missing values for Saturdays and Sundays filled in with the corresponding stock names and previous day’s prices.

Conclusion

In this article, we explored how to fill null values in time series data for weekends using SQL. We used a combination of cross join, left join, and lag() functions to achieve our goal. By following these steps, you should be able to tackle similar problems involving missing values in your own datasets.

Additional Considerations

This solution assumes that you’re working with a relational database management system like MySQL or PostgreSQL. For other databases, such as Microsoft SQL Server or Oracle, the exact syntax might vary slightly. Always refer to the documentation of your specific database for detailed instructions on using these functions.

Additionally, this approach may not work correctly if your data is too large to fit into memory. In that case, consider implementing a more efficient solution, like processing the data in chunks or utilizing parallel processing techniques.

If you have any further questions or need additional help with similar problems, feel free to ask!

Last modified on 2023-09-08