BurstDetection

Burst detection refers to identifying periods of heightened activity in time-stamped event streams, during which events occur in rapid succession relative to a baseline rate. Such bursts can indicate important underlying processes, from information diffusion to abnormal system behavior.

Approaches vary, but common methods include Kleinberg's burst detection algorithm, which models the stream as a multi-state automaton with different burst levels and infers transitions from data; CUSUM and other change-point techniques; Bayesian online change-point detection; and self-exciting Hawkes process models that capture clustering of events. These methods aim to assign segments of time to different intensity levels and mark transitions when activity changes significantly.

Applications span many domains: social media analytics for surges in posts or mentions; cybersecurity and network monitoring for bursts in traffic or connections; neuroscience for bursts of neuronal firing in spike trains; finance for bursts in trading volume or order flow; and environmental sensing for episodic event clusters. Burst detection helps summarize complex time series, support anomaly detection, and reveal mechanisms driving rapid changes in activity.

Key challenges include selecting an appropriate time scale, handling noise and nonstationarity, avoiding over- or under-detection, and validating results when ground truth is unavailable. Software implementations exist in statistical and data-mining toolkits, and burst detection is often integrated into broader anomaly detection or event-detection workflows.