---

name: anomaly-detector description: Detect anomalies in data using statistical and ML methods. Z-score, IQR, Isolation Forest, and time-series anomalies. allowed-tools: Read Write Edit Bash

Anomaly Detector

Audience: Data engineers and analysts detecting outliers in datasets.

Goal: Provide production-ready anomaly detection functions for various data types.

Scripts

Execute detection functions from scripts/anomaly_detection.py:

Copyfrom scripts.anomaly_detection import (
    detect_anomalies_zscore,
    detect_anomalies_iqr,
    detect_anomalies_modified_zscore,
    detect_anomalies_isolation_forest,
    detect_anomalies_lof,
    detect_anomalies_rolling,
    detect_anomalies_stl,
    detect_anomalies_ensemble
)

Method Selection

Usage Examples

Single Column Detection

Copyimport pandas as pd
from scripts.anomaly_detection import detect_anomalies_zscore, detect_anomalies_iqr

df = pd.read_csv('data.csv')

# Z-score method (good for normal distributions)
anomalies_z = detect_anomalies_zscore(df['value'], threshold=3.0)

# IQR method (robust to skewed data)
anomalies_iqr = detect_anomalies_iqr(df['value'], multiplier=1.5)

print(f"Z-score found {anomalies_z.sum()} anomalies")
print(f"IQR found {anomalies_iqr.sum()} anomalies")

Multi-Column with Isolation Forest

Copyfrom scripts.anomaly_detection import detect_anomalies_isolation_forest

numeric_cols = ['revenue', 'quantity', 'price']
anomalies = detect_anomalies_isolation_forest(df, numeric_cols, contamination=0.01)

df_anomalies = df[anomalies]

Ensemble Approach (Recommended)

Copyfrom scripts.anomaly_detection import detect_anomalies_ensemble

results = detect_anomalies_ensemble(
    df,
    columns=['revenue', 'quantity'],
    methods=['zscore', 'iqr', 'isolation_forest'],
    min_agreement=2  # Flag if 2+ methods agree
)

confirmed_anomalies = df[results['is_anomaly']]

Time-Series Anomalies

Copyfrom scripts.anomaly_detection import detect_anomalies_rolling, detect_anomalies_stl

# Rolling window (for trending data)
anomalies = detect_anomalies_rolling(df['daily_sales'], window=7, n_std=2.0)

# STL decomposition (for seasonal data)
anomalies = detect_anomalies_stl(df['monthly_revenue'], period=12, threshold=3.0)

Dependencies

Copypandas
numpy
scikit-learn  # For Isolation Forest, LOF
statsmodels   # For STL decomposition