@tpmjs/tools-anomaly-detect-mad

Detect anomalies (outliers) in numeric data using the Median Absolute Deviation (MAD) method.

Overview

The Median Absolute Deviation (MAD) is a robust statistic for detecting outliers. Unlike standard deviation-based methods (which are themselves influenced by outliers), MAD is resistant to extreme values, making it more reliable for anomaly detection.

Why MAD?

Traditional approach (standard deviation):

• Outliers inflate the standard deviation
• This makes it harder to detect those same outliers
• Assumes normal distribution

MAD approach:

• Uses median (not mean) - resistant to outliers
• MAD itself is calculated from medians - doubly robust
• No distribution assumptions
• More reliable in real-world data with extreme values

Installation

npm install @tpmjs/tools-anomaly-detect-mad

Usage with AI SDK

import { anomalyDetectMADTool } from '@tpmjs/tools-anomaly-detect-mad';
import { generateText } from 'ai';

const result = await generateText({
  model: yourModel,
  tools: { detectAnomalies: anomalyDetectMADTool },
  toolChoice: 'required',
  prompt: 'Find anomalies in this data: [10, 12, 11, 13, 10, 95, 12, 11, 14, 10]',
});

Direct Usage

import { anomalyDetectMADTool } from '@tpmjs/tools-anomaly-detect-mad';

const result = await anomalyDetectMADTool.execute({
  data: [10, 12, 11, 13, 10, 95, 12, 11, 14, 10],
  threshold: 3.5, // Optional, defaults to 3.5
});

console.log(result);
// {
//   anomalies: [
//     {
//       value: 95,
//       index: 5,
//       deviation: 83.5,
//       zScore: 28.177
//     }
//   ],
//   anomalyIndices: [5],
//   statistics: {
//     median: 11.5,
//     mad: 2,
//     threshold: 3.5,
//     totalPoints: 10,
//     anomalyCount: 1,
//     anomalyPercentage: 10
//   }
// }

Parameters

• data (required): Array of numeric values to analyze (minimum 3 values)
• threshold (optional): Modified z-score threshold for anomaly detection
  • Default: 3.5 (recommended, equivalent to ±3σ in normal distribution)
  • Range: 0.1 to 10
  • Lower values = more sensitive (detects more anomalies)
  • Higher values = more conservative (detects fewer, more extreme anomalies)

Threshold Guidelines

Returns

{
  anomalies: Array<{
    value: number;      // The anomalous value
    index: number;      // Position in original array
    deviation: number;  // Absolute deviation from median
    zScore: number;     // Modified z-score (based on MAD)
  }>;
  anomalyIndices: number[];  // Quick array of anomaly positions
  statistics: {
    median: number;            // Median of dataset
    mad: number;               // Median Absolute Deviation
    threshold: number;         // Threshold used
    totalPoints: number;       // Total data points
    anomalyCount: number;      // Number of anomalies found
    anomalyPercentage: number; // Percentage of data that are anomalies
  };
}

Anomalies are sorted by absolute z-score (most extreme first).

Algorithm

The MAD method works as follows:

1. Calculate Median: M = median(data)
2. Calculate Absolute Deviations: |x_i - M| for each data point
3. Calculate MAD: MAD = median(|x_i - M|)
4. Calculate Modified Z-Score: z_i = 0.6745 × (x_i - M) / MAD
5. Flag Anomalies: Points where |z_i| > threshold

The constant 0.6745 is the 75th percentile of the standard normal distribution, which makes the MAD-based z-score comparable to traditional z-scores.

Example Use Cases

Server response times:

const responseTimes = [120, 115, 130, 125, 118, 3500, 122, 119, 128, 121];
const result = await anomalyDetectMADTool.execute({ data: responseTimes });
// Detects the 3500ms outlier

Sensor readings with noise:

const temperatures = [20.1, 20.3, 19.9, 20.2, 45.0, 20.0, 19.8, 20.4];
const result = await anomalyDetectMADTool.execute({
  data: temperatures,
  threshold: 3.0, // More sensitive for safety-critical applications
});
// Detects the 45.0 degree spike

Financial transactions:

const transactions = [25.50, 32.10, 28.75, 31.20, 2500.00, 29.80];
const result = await anomalyDetectMADTool.execute({ data: transactions });
// Flags the unusual $2500 transaction

Quality control:

const measurements = [10.02, 10.01, 9.99, 10.00, 10.02, 10.50, 10.01];
const result = await anomalyDetectMADTool.execute({
  data: measurements,
  threshold: 2.5, // Sensitive to detect quality issues early
});
// Detects measurements outside acceptable tolerance

Edge Cases

All values identical:

const data = [5, 5, 5, 5, 5];
const result = await anomalyDetectMADTool.execute({ data });
// Returns: anomalyCount: 0, mad: 0

MAD = 0 with variation:

const data = [10, 10, 10, 10, 15]; // Median = 10, but one different value
const result = await anomalyDetectMADTool.execute({ data });
// Special handling: flags the 15 as anomaly with zScore: Infinity

Comparison: MAD vs Standard Deviation

Consider the dataset: [10, 12, 11, 13, 10, 95, 12, 11, 14, 10]

Standard Deviation Method:

• Mean = 19.8
• StdDev = 25.4 (inflated by the outlier!)
• Z-score of 95 = (95-19.8)/25.4 = 2.96
• May not flag as outlier (typically use threshold = 3)

MAD Method:

• Median = 11.5
• MAD = 2 (robust!)
• Modified Z-score of 95 = 28.2
• Clearly flags as outlier (threshold = 3.5)

When to Use MAD

Use MAD when:

• Data may contain outliers (most real-world data)
• Distribution is unknown or non-normal
• Need robust detection resistant to contamination
• Small to medium sample sizes

Consider alternatives when:

• Data is known to be normally distributed
• Very large datasets (computational efficiency matters)
• Need parametric statistical inference

Performance Considerations

• Time Complexity: O(n log n) due to sorting for median calculation
• Space Complexity: O(n) for storing sorted arrays
• Recommended: Works well for datasets up to 100,000+ points

References

• Leys, C., et al. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764-766
• Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the median absolute deviation. Journal of the American Statistical Association, 88(424), 1273-1283
• Iglewicz, B., & Hoaglin, D. C. (1993). How to Detect and Handle Outliers. ASQC Quality Press

License

MIT