Mastering Moving Averages in SQL: Analyzing Trends with T-SQL

Comparing Two Moving Averages in SQL

Analyzing data trends is a crucial aspect of data-driven decision-making. One common way to do this is by calculating moving averages. In SQL, we often need to compare two moving averages to derive insights from our data. In this blog, we'll explore how to compare two moving averages using T-SQL 2012, using a sample dataset, and discuss the advantages, disadvantages, best practices, and use cases.

The Sample Dataset

Let's work with a simplified product sales dataset. Here's a snippet of the table structure:


productid   productname           supplierid         categoryid      unitprice       discontinued

1                Product HHYDP      1                      1                     18.00             0

2                Product RECZE       1                      1                     19.00             0

3                Product IMEHJ        1                      2                     10.00             0

4                Product KSBRM      2                      2                     22.00             0

5                Product EPEIM         2                     2                     21.35             1

6                Product VAIIV           3                    2                     25.00             0

7                Product HMLNI         3                    7                    30.00             0

8                Product WVJFP          3                    2                    40.00             0

9                Product AOZBW        4                    6                     97.00             1

10              Product YHXGE         4                    8                     31.00             0

Calculating Moving Averages

We'll calculate the moving average of the `unitprice` for each product. For simplicity, let's calculate a 3-day moving average. Here's the T-SQL code for it:



SELECT
        productid,
        productname,
        unitprice,
        LAG(unitprice, 1) 

            OVER (PARTITION BY supplierid ORDER BY productid) AS day1,
        LAG(unitprice, 2) 

            OVER (PARTITION BY supplierid ORDER BY productid) AS day2,
        AVG(unitprice) 

            OVER (PARTITION BY supplierid 

        ORDER BY productid ROWS BETWEEN 2 PRECEDING AND 

        CURRENT ROW) AS three_day_avg
FROM [Production].[Products]


In this query, we use the `LAG` function to fetch the unit price for the previous days (day1 and day2) and the `AVG` window function to calculate the 3-day moving average.

Advantages and Disadvantages

Advantages:

1. Data Insights: Moving averages provide a smooth trendline, making it easier to spot trends and patterns in your data.


2. Noise Reduction: Moving averages help reduce the impact of daily fluctuations, making it simpler to identify underlying trends.


3. Decision Support: Comparing two moving averages can help you make data-driven decisions, such as when to buy or sell products based on their price trends.

Disadvantages:

1. Delayed Signals: Moving averages can be slow to respond to rapid changes in data since they smooth the data over a specific time period.


2. Complex Queries: Calculating moving averages can be computationally intensive, especially for large datasets.

Best Practices

- Choose the right moving average type (e.g., simple, exponential) based on the nature of your data.


- Select an appropriate time window for your moving averages; this depends on the specific problem you are addressing.


- Regularly update your moving averages to ensure they reflect current trends.

Use Cases

-Stock Market Analysis: Traders use moving averages to predict future stock prices.


- Demand Forecasting: Businesses use moving averages to predict future product demand.


- Web Traffic Analysis: Website owners use moving averages to understand user engagement trends.


- Quality Control: Manufacturers use moving averages to monitor product quality over time.

Conclusion

 

Comparing two moving averages in SQL can be a valuable tool for understanding trends and patterns in your data. Whether you're analyzing stock prices or product sales, this technique can help you make more informed decisions and spot important trends. Just remember to choose the right moving average type, set your time window carefully, and keep your queries optimized for performance.