Time series data is a sequence of observations indexed in time order, typically recorded at regular or irregular intervals from sensors, systems or markets. Its temporal structure exposes trends, seasonality and autocorrelation that distinguish it from cross-sectional data and demand specialised storage, querying and analysis. Time series underpin monitoring, forecasting and anomaly detection across many domains.
Overview
- Time series workloads are dominated by high-volume appends and time-bounded range queries, motivating purpose-built time-series databases with compression and downsampling. Analysis exploits temporal dependence to model trend and seasonality and to forecast or flag anomalies.
Key aspects
- Observations ordered and indexed by time
- Trend, seasonality and autocorrelation structure
- Regular and irregular sampling intervals
- Append-heavy ingestion and range-query patterns
- Compression, retention and downsampling strategies
Applications
- Infrastructure and application monitoring via telemetry
- Forecasting demand, load and financial series
- Anomaly detection in sensor and IoT streams
- Real-time analytics over streaming metrics