Time Series Analysis with SQL

Unlocking Insights over Time

In today’s data-driven world, understanding how trends change over time is crucial for making informed business decisions.

Whether you’re analyzing stock prices, website traffic, or sales data, time series analysis helps uncover patterns, predict future values, and understand seasonal effects.

While SQL (Structured Query Language) is widely known for querying relational databases, it’s also a powerful tool for time series analysis. In this article, we will explore how SQL can be used for time series analysis, accompanied by a practical case study.

What is Time Series Analysis?

Time series analysis involves the examination of data points collected or recorded at successive points in time. Unlike other types of data, time series data is temporal, meaning the order of the data points matters. Common use cases for time series analysis include stock market trends, weather forecasting, and financial analysis. The goal is to detect patterns such as trends, seasonal effects, or cyclical behaviour and to forecast future values.

SQL and Time Series Data

SQL is known for its ability to query and manipulate relational databases, but it also has several functions that make it ideal for time series analysis. Some common SQL functionalities used in time series analysis include:

• Date and Time Functions: SQL’s date and time functions allow you to extract specific components of time (year, month, week, day) or perform calculations on date fields.
• Window Functions: These functions allow you to calculate cumulative sums, moving averages, and lag or lead values, which are essential for analyzing trends over time.
• Aggregations: Grouping data by time intervals (such as days, weeks, or months) helps to summarize data for analysis.

Let’s explore these concepts through a practical case study.

Case Study: Sales Data Analysis for a Retail Store

Scenario:

Imagine you are a data analyst at a retail store. Your company wants to analyze sales trends over the past year to make inventory decisions and forecast sales for the next quarter. The dataset contains sales information with the following columns:

• sales_date: The date of the sale
• product_id: The ID of the product sold
• units_sold: The number of units sold
• revenue: The revenue generated from the sale

Your task is to perform a time series analysis to uncover monthly sales trends and project future sales.

Step 1: Analyzing Monthly Sales Trends

To analyze monthly trends, we need to aggregate the data by month and calculate the total number of units sold and total revenue generated for each month.

SELECT 
    DATE_TRUNC('month', sales_date) AS sales_month,
    SUM(units_sold) AS total_units_sold,
    SUM(revenue) AS total_revenue
FROM sales_data
GROUP BY sales_month
ORDER BY sales_month;        

Here’s what’s happening:

• DATE_TRUNC('month', sales_date) truncates the sales_date to the start of each month.
• SUM(units_sold) and SUM(revenue) calculate the total number of units sold and revenue for each month.
• We group the data by the truncated month and order the results chronologically.

Step 2: Identifying Year-Over-Year Growth

To identify year-over-year growth, we can use SQL’s window functions to compare sales figures from the current year to the previous year.

WITH monthly_sales AS (
    SELECT 
        DATE_TRUNC('month', sales_date) AS sales_month,
        SUM(revenue) AS total_revenue
    FROM sales_data
    GROUP BY sales_month
)
SELECT
    sales_month,
    total_revenue,
    LAG(total_revenue, 12) OVER (ORDER BY sale_month) AS last_year_revenue,
    (total_revenue - LAG(total_revenue, 12) OVER (ORDER BY sale_month)) / 
      NULLIF(LAG(total_revenue, 12) OVER (ORDER BY sale_month), 0) * 100 
      AS yoy_growth
FROM monthly_sales;        

• LAG: This function retrieves the total revenue from 12 months (one year) before the current row.
• yoy_growth: Calculates the year-over-year growth by comparing the current month’s revenue to the previous year’s corresponding month.

Step 3: Forecasting Future Sales with a Moving Average

Moving averages help smooth out short-term fluctuations and highlight longer-term trends. We’ll calculate a 3-month moving average to forecast future sales.

WITH monthly_sales AS (
    SELECT 
        DATE_TRUNC('month', sales_date) AS sales_month,
        SUM(units_sold) AS total_units_sold
    FROM sales_data
    GROUP BY sales_month
)
SELECT 
    sales_month,
    total_units_sold,
    AVG(total_units_sold) OVER (ORDER BY sales_month ROWS BETWEEN 2 
      PRECEDING AND CURRENT ROW) AS moving_avg_3_month
FROM monthly_sales;        

• ROWS BETWEEN 2 PRECEDING AND CURRENT ROW: This specifies the 3-month window (current month and the two preceding months) for calculating the moving average.

Step 4: Analyzing Seasonality

If our retail store has seasonal products, analyzing sales seasonality is crucial. Let’s calculate the average sales for each month across multiple years to detect seasonal patterns.

SELECT 
    EXTRACT(MONTH FROM sales_date) AS month,
    AVG(units_sold) AS avg_units_sold
FROM sales_data
GROUP BY month
ORDER BY month;        

This query extracts the month from the sale date and calculates the average number of units sold in each month across all years.

Conclusion

Time series analysis with SQL provides a powerful way to uncover trends, identify growth opportunities, and forecast future outcomes. In this case study, we used SQL to analyze retail sales data over time, performing monthly trend analysis, calculating year-over-year growth, forecasting sales with a moving average, and identifying seasonality.

SQL’s ability to manipulate and analyze time-related data makes it a versatile tool for data analysts and business professionals. Whether you’re working with sales data, financial records, or customer behaviour, SQL can help you unlock valuable insights from your time series data.

By combining SQL’s powerful querying capabilities with time series analysis techniques, you can build robust data-driven strategies for your business. Let this case study inspire you to explore more with your own datasets.