Analyzing Time-Series Data in SQL: Techniques for Temporal Queries

Table of Contents

1. Introduction
2. What Is Time-Series Data?
3. Common Use Cases for Time-Based Analysis
4. SQL Data Types for Timestamps
5. Formatting and Parsing Date/Time Values
6. Filtering Data by Time Range
7. Truncating Time Granularity (DATE_TRUNC, TO_CHAR)
8. Grouping by Day, Week, Month
9. Calculating Running Totals and Rolling Averages
10. Using LAG() and LEAD() for Time Comparisons
11. Time Gap Detection Between Events
12. Handling Missing Time Intervals
13. Binning Time into Fixed Buckets
14. Time-Based Joins (e.g., Point-in-Time Snapshots)
15. Creating Time Windows with RANGE or ROWS
16. Using INTERVAL for Dynamic Time Offsets
17. Indexing Strategies for Time-Series Data
18. Real-World Use Case: User Logins Over Time
19. Best Practices for Time-Series SQL
20. Summary and What’s Next

1. Introduction

Time-series data represents chronological records of events or measurements. SQL provides powerful tools to query, transform, and analyze such data for dashboards, reporting, and forecasting.

2. What Is Time-Series Data?

Time-series data is a sequence of values recorded over time. Examples include:

• Sensor readings (IoT)
• Stock prices
• Web traffic logs
• Temperature records
• Financial transactions

Each row typically contains a timestamp and a measured value.

3. Common Use Cases for Time-Based Analysis

• Trends over time
• Month-over-month comparisons
• User activity heatmaps
• Downtime detection
• Forecasting and seasonal analysis

4. SQL Data Types for Timestamps

5. Formatting and Parsing Date/Time Values

PostgreSQL:

sqlCopyEditTO_CHAR(timestamp, 'YYYY-MM-DD')

MySQL:

sqlCopyEditDATE_FORMAT(timestamp, '%Y-%m-%d')

Useful for human-readable outputs or time grouping.

6. Filtering Data by Time Range

sqlCopyEditSELECT * FROM logins
WHERE login_time BETWEEN '2024-01-01' AND '2024-01-31';

Use >= and < instead of BETWEEN for precise range control.

7. Truncating Time Granularity

PostgreSQL:

sqlCopyEditSELECT DATE_TRUNC('day', login_time) AS day, COUNT(*)
FROM logins
GROUP BY 1;

MySQL:

sqlCopyEditSELECT DATE(login_time) AS day, COUNT(*)
FROM logins
GROUP BY day;

8. Grouping by Day, Week, Month

sqlCopyEdit-- Monthly active users
SELECT DATE_TRUNC('month', signup_date) AS month, COUNT(*)
FROM users
GROUP BY month
ORDER BY month;

Use WEEK, MONTH, HOUR, etc. based on granularity needed.

9. Calculating Running Totals and Rolling Averages

sqlCopyEditSELECT date,
       SUM(sales) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total,
       AVG(sales) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_7day_avg
FROM sales_data;

Window functions make rolling aggregates simple and powerful.

10. Using LAG() and LEAD() for Time Comparisons

sqlCopyEditSELECT date, sales,
       LAG(sales) OVER (ORDER BY date) AS previous_day_sales,
       sales - LAG(sales) OVER (ORDER BY date) AS daily_change
FROM sales_data;

Ideal for day-over-day or period-over-period analysis.

11. Time Gap Detection Between Events

sqlCopyEditSELECT user_id, activity_time,
       LAG(activity_time) OVER (PARTITION BY user_id ORDER BY activity_time) AS previous_time,
       activity_time - LAG(activity_time) OVER (...) AS time_diff
FROM activity_logs;

Detects inactivity gaps, session timeouts, etc.

12. Handling Missing Time Intervals

Use a calendar or date series table to join:

sqlCopyEdit-- Generate days and LEFT JOIN actual data to fill gaps
SELECT d.day, COUNT(logins.id)
FROM generate_series('2024-01-01'::date, '2024-01-31', interval '1 day') d(day)
LEFT JOIN logins ON DATE(logins.login_time) = d.day
GROUP BY d.day;

13. Binning Time into Fixed Buckets

sqlCopyEditSELECT FLOOR(EXTRACT(EPOCH FROM event_time) / 300) * 300 AS bucket,
       COUNT(*)
FROM events
GROUP BY bucket;

This groups data into 5-minute buckets (300s).

14. Time-Based Joins (e.g., Point-in-Time Snapshots)

sqlCopyEditSELECT p.id, p.name, r.region
FROM people p
JOIN regions r ON p.snapshot_time BETWEEN r.start_time AND r.end_time;

Useful for tracking temporal dimensions like active contracts or locations.

15. Creating Time Windows with RANGE or ROWS

sqlCopyEditSELECT date, SUM(sales) OVER (
  ORDER BY date
  RANGE BETWEEN INTERVAL '7 days' PRECEDING AND CURRENT ROW
) AS weekly_sales
FROM sales_data;

RANGE is value-based, while ROWS is row-count-based.

16. Using INTERVAL for Dynamic Time Offsets

sqlCopyEditSELECT * FROM purchases
WHERE purchase_time >= NOW() - INTERVAL '30 days';

Dynamic filters for last X days/hours/weeks.

17. Indexing Strategies for Time-Series Data

• Use B-tree indexes on timestamp columns
• Composite indexes: (timestamp, user_id)
• Consider partitioning large datasets by time range
• Use descending indexes for reverse chronological queries

18. Real-World Use Case: User Logins Over Time

sqlCopyEditSELECT DATE_TRUNC('week', login_time) AS week, COUNT(*) AS logins
FROM user_logins
WHERE login_time >= CURRENT_DATE - INTERVAL '3 months'
GROUP BY week
ORDER BY week;

Used for weekly user activity trends.

19. Best Practices for Time-Series SQL

• Store time in UTC
• Avoid redundant DATE() or CAST() in filters — may prevent index use
• Always index time fields in high-volume logs
• Use materialized views for pre-aggregated reports
• Benchmark with real volumes to test query plans

20. Summary and What’s Next

SQL offers powerful techniques for analyzing time-series data — from simple grouping to complex windowing functions. Mastering temporal queries equips you to build advanced analytics on user behavior, trends, forecasts, and system events.