Before you can detect what’s wrong, you need to understand what’s normal.
Part 2 of our three-part guide focuses on how to model the “normal” behavior of time series data — a critical step in detecting anomalies accurately and at scale.
“There are many patterns and distributions that are inherent to data. An anomaly detection system must model the data, but a single model does not fit all metrics.”
What’s inside:
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Why assuming a single model or distribution is not enough
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How seasonality and changing patterns impact model accuracy
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Techniques for real-time, adaptive learning at scale
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Why automated, auto-tuned algorithms are essential for modern systems
