Welcome to DTAIAnomaly’s documentation!

DTAIAnomaly is an easy-to-use Python package for time series anomaly detection. Its main goal is to offer a simple interface to detect anomalies in time series data using state-of-the-art methods. Additionally, DTAIAnomaly provides a benchmark framework to quantitatively evaluate time series anomaly detection methods on a large repository of time series.

The source is available on GitLab.

Getting started

• Installation
• Data Management
• Anomaly detection
• Evaluation
• Large Scale Experiments

Documentation

• Anomaly detection module
• Data management module
• Evaluation module
• Visualization module
• Workflow module