time series

Back to DuckDB Data Engineering Glossary

A time series is a sequence of data points collected and ordered chronologically at regular intervals. In the context of data analysis and engineering, time series data often represents measurements or observations of a particular phenomenon over time, such as stock prices, temperature readings, or website traffic. Time series data is characterized by its temporal nature, where the order and spacing of data points are crucial for understanding trends, patterns, and seasonality.

Time series analysis involves various techniques to extract meaningful insights from this type of data, including:

1. Trend analysis: Identifying long-term patterns or directions in the data.
2. Seasonality detection: Recognizing recurring patterns at fixed intervals.
3. Forecasting: Predicting future values based on historical data.

When working with time series data in DuckDB, you can leverage built-in functions like date_trunc() for grouping data into specific time intervals and window functions for calculating moving averages or cumulative sums. For example:

Copy code
-- Calculate a 7-day moving average of daily sales
SELECT 
    date,
    sales,
    AVG(sales) OVER (
        ORDER BY date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) AS moving_avg
FROM daily_sales
ORDER BY date;

Time series data is commonly used in various fields, including finance, meteorology, economics, and IoT applications. Specialized time series databases like InfluxDB or TimescaleDB are designed to efficiently store and query this type of data at scale.