Anomaly Detection for Monitoring

Table of Contents
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Why Anomaly Detection? 2
The Many Kinds of Anomaly Detection 4
Conclusions 6
2. A Crash Course in Anomaly Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . 9
A Real Example of Anomaly Detection 10
What Is Anomaly Detection? 11
What Is It Good for? 11
How Can You Use Anomaly Detection? 13
Conclusions 14
3. Modeling and Predicting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Statistical Process Control 16
More Advanced Time Series Modeling 24
Predicting Time Series Data 25
Evaluating Predictions 27
Common Myths About Statistical Anomaly Detection 27
Conclusions 31
4. Dealing with Trends and Seasonality. . . . . . . . . . . . . . . . . . . . . . . . . . 33
Dealing with Trend 34
Dealing with Seasonality 35
Multiple Exponential Smoothing 36
Potential Problems with Predicting Trend and Seasonality 37
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