This tutorial explains how to automatically locate trend change points in large collections of time series by combining grid search with piecewise regression, covering the underlying theory, cost functions, implementation in R and Python, and practical constraints to avoid over‑fitting.
This article shows how to turn change‑point detection into an optimization problem by exhaustively searching knot configurations with grid search, selecting the best model using a penalised likelihood criterion (BIC), and applying piecewise regression to automatically locate trend breakpoints, illustrated with R and Python code on California natural‑gas consumption data.
Goldeneye, Alibaba Mom's monitoring platform, uses big‑data pipelines, dynamic threshold prediction, mean‑shift change‑point detection, and automated metric discovery to replace manual alarm settings, reduce false alerts, and provide intelligent, scalable business monitoring across hundreds of services.
