Time-series data is a sequence of data points indexed in time order. These data points typically consist of successive measurements made from the same source over a time interval and are used to track change over time.
Common examples include: stock prices, temperature readings, sensor data, server metrics, IoT device measurements, and network traffic patterns.
Every data point has a precise timestamp indicating when it was recorded.
Data points are ordered chronologically and the sequence matters.
Often collected at regular intervals with potentially millions of points per day.
Typically consists of numeric measurements or categorical observations.
Specialized databases optimized for time-series data:
A typical time-series data point consists of:
2025-12-26T10:30:00Z)temperature, cpu_usage)23.5, 75.2)location=server1, region=us-east)
temperature,location=server1,region=us-east value=23.5 1735208400000000000
Efficiently write massive volumes of time-stamped data points.
Retrieve data by time range, aggregations, and filters.
Compute averages, sums, min/max over time windows.
Detect anomalies and trigger notifications based on thresholds.
Predict future values using statistical models and ML.
Reduce data resolution to save storage while preserving trends.