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#anomaly-detection

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01 ·Time Series·★ MEMBER·PAPER·11 min read Time-Series Anomaly Detection — The Math Behind the Alerts An alert should fire on the gap between what you observed and what that moment predicted — not on the raw size of a number. Four rulers for measuring that gap (robust statistics, forecast residuals, subsequence distance, changepoints), built up from analogy to formula, then the two things that actually break in production: how you pick the threshold and how you evaluate. 02 ·CNNs & Image Recognition·★ MEMBER·11 min read Anomaly Detection from Scratch — Learning From Normal Alone Why an AI can learn from good units alone on a factory floor where defect samples barely exist. Two rulers — reconstruction error and density estimation — built up from metaphor to equations, all the way to the part that decides everything in practice: choosing the threshold.