How to Build a Full‑Chain Metric Anomaly Detection Framework for Business Operations

This article explains how to design a complete metric‑abnormality pipeline—from real‑time threshold alerts and statistical tests such as 3σ, GESD, IQR, and MBP to trend analysis with Mann‑Kendall and Prophet, and finally to deterministic and probabilistic attribution using contribution decomposition and SHAP, all illustrated with practical business cases.

Business AnalyticsProphet modelSHAP

0 likes · 20 min read

How to Build a Full‑Chain Metric Anomaly Detection Framework for Business Operations

DataFunSummit

Dec 19, 2023 · Big Data

Metric Anomaly Detection and Diagnosis Practices at NetEase Yanxuan

This article presents NetEase Yanxuan's end‑to‑end approach for automatically detecting and diagnosing metric anomalies in e‑commerce, covering background motivation, three types of anomalies, statistical detection frameworks (GESD, volatility, Mann‑Kendall), post‑processing, contribution‑decomposition methods, dimension‑explosion challenges, optimization techniques, and a brief Q&A.

contribution analysisdiagnosticsmetric anomaly detection

0 likes · 18 min read

Metric Anomaly Detection and Diagnosis Practices at NetEase Yanxuan

Data Thinking Notes

Dec 7, 2023 · Big Data

How NetEase Yanxuan Detects and Diagnoses Metric Anomalies at Scale

This article explains NetEase Yanxuan's end‑to‑end practice for automatically detecting, classifying, and diagnosing metric anomalies in e‑commerce, covering background motivation, three anomaly types, statistical detection frameworks (GESD, volatility, trend), post‑processing, contribution‑decomposition methods, dimension‑explosion challenges, and practical optimizations.

contribution decompositiondata monitoringe‑commerce

0 likes · 20 min read

How NetEase Yanxuan Detects and Diagnoses Metric Anomalies at Scale

DataFunTalk

Nov 8, 2023 · Fundamentals

Metric Anomaly Detection and Diagnosis Practices at NetEase Yanxuan

This article presents NetEase Yanxuan's end‑to‑end approach for automatically detecting and diagnosing metric anomalies in e‑commerce, covering background motivations, statistical detection methods (absolute, volatility, trend), contribution‑decomposition diagnosis, optimization techniques for dimensional explosion, and a Q&A on practical implementation.

Diagnostic Analysiscontribution decompositione-commerce analytics

0 likes · 17 min read