How Can I Simulate a Lag Function in MySQL for Time-Series Analysis of Stock Quotes?

MySQL Time Series Analysis: Simulating the LAG Function for Stock Quote Comparisons

MySQL's lack of a built-in LAG function can complicate time series analysis, particularly when comparing sequential stock quotes. This article demonstrates a workaround to effectively simulate the LAG function and calculate quote variations over time for multiple companies.

Our approach uses a clever technique:

SET @quot = -1;
SELECT time,
       company,
       @quot AS lag_quote,
       @quot := quote AS curr_quote
FROM stocks
ORDER BY company, time;

The @quot variable cleverly handles both:

• lag_quote: The quote from the preceding row.
• curr_quote: The current row's quote.

Ordering the results by company and time ensures accurate comparisons within each company's data.

To achieve the desired output format (as potentially requested in the original question), we use nested queries:

SET @quot = 0, @latest = 0, @comp = '';
SELECT B.*
FROM (
    SELECT A.time,
           A.change,
           IF(@comp = A.company, 1, 0) AS LATEST,
           @comp := A.company AS company
    FROM (
        SELECT time,
               company,
               quote - @quot AS change,
               @quot := quote AS curr_quote
        FROM stocks
        ORDER BY company, time
    ) A
    ORDER BY company, time DESC
) B
WHERE B.LATEST = 1;

This nested query structure efficiently identifies the most recent quote change for each company, presenting the data in a structured manner.

This method offers a practical and efficient solution for simulating the LAG function in MySQL, particularly valuable when a native LAG function isn't available. It's a powerful technique for various time-series analysis tasks.

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