A Practical Toolkit for Time Series Anomaly Detection Using Python

🔍 A Practical Toolkit for Time Series Anomaly Detection Using Python

Working with time series and need to detect unusual behavior? 📈

This article builds a modular Python toolbox to identify 4 types of anomalies in a bank of signals:

📉 1. Trend anomaly Linear + polynomial regression. If p < 0.05, there’s a significant trend.

📊 2. Volatility anomaly Trend is removed first, then each series’ variance is compared against the dataset. Outliers are flagged.

📍 3. Single-point anomaly Rolling window + Z-score. If a point exceeds 3 standard deviations from its local neighborhood, it’s anomalous.

📦 4. Dataset-level anomaly Each series is compressed to a baseline (median) and compared across the bank. Detects signals with unusual magnitude.

💡 Quick explanation

Before reaching for LSTMs or Transformers, this classical statistics toolkit gives you explainable, scalable, and fast-to-implement results. A perfect baseline before moving on to heavier models.

More information at the link 👇

GitHub - PieroPaialungaAI/timeseriesanomaly: Collection of anomaly detection methods for time series

Collection of anomaly detection methods for time series - PieroPaialungaAI/timeseriesanomaly

github.com ↗

A Practical Toolkit for Time Series Anomaly Detection, Using Python | Towards Data Science

Here's how to detect point anomalies within each series, and identify anomalous signals across the whole bank

towardsdatascience.com ↗