# Anomaly Detection

**Category:** metrics  
**Short Description:** The systematic process of identifying data points that significantly deviate from expected patterns and thresholds.  
**Last Updated:** 2024-03-15

## Definition

Anomaly detection is the systematic process of identifying data points that deviate significantly from expected patterns using statistical methods and machine learning. In digital advertising, it's crucial for detecting performance issues, fraud, tracking problems, and other irregularities that require immediate attention. The process typically involves establishing baseline performance patterns, setting statistical thresholds, and automatically flagging deviations that exceed normal variance ranges.

## Calculation

**Formula:** `Typically uses statistical methods like Z-score (z = (x - μ) / σ) or machine learning models`

**Explanation:** Compares current values against historical patterns, flagging significant deviations based on standard deviations or learned patterns. Common thresholds include ±2 or ±3 standard deviations from the mean.

### Components

- **Current Value**: The metric value being evaluated
- **Mean**: The mean of historical values within the comparison window
- **Standard Deviation**: The standard deviation of historical values

## Examples

- Detecting sudden CPC spikes indicating auction manipulation
- Identifying unusual conversion patterns suggesting tracking issues
- Flagging statistically improbable click-through rates above 10%
- Monitoring for sudden drops in conversion tracking
- Identifying unusual geographic or temporal patterns in traffic

## Related Terms

### Component Terms

- **[Standard Deviation](/resources/glossary/metrics/standard-deviation)**: Helps establish normal ranges for anomaly detection
- **[Moving Average](/resources/glossary/metrics/moving-average)**: Provides baseline for detecting deviations
- **[Statistical Significance](/resources/glossary/metrics/statistical-significance)**: Helps validate if detected anomalies are statistically meaningful
- **[Variance](/resources/glossary/metrics/variance)**: Helps establish expected ranges of fluctuation