tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs

TL;DR

The paper introduces 'tegdet', an extensible Python library that uses time-evolving graphs for unsupervised anomaly detection in univariate time series, offering multiple dissimilarity metrics and promising accuracy and efficiency.

Contribution

It presents a novel, extendable Python library leveraging dynamic graphs for anomaly detection, with comprehensive metrics and guidelines for improved performance.

Findings

28 dissimilarity metrics implemented

Library is uniquely extendable with new techniques

Promising results in accuracy and execution time

Abstract

This paper presents a new Python library for anomaly detection in unsupervised learning approaches. The input for the library is a univariate time series representing observations of a given phenomenon. Then, it can identify anomalous epochs, i.e., time intervals where the observations are above a given percentile of a baseline distribution, defined by a dissimilarity metric. Using time-evolving graphs for the anomaly detection, the library leverages valuable information given by the inter-dependencies among data. Currently, the library implements 28 different dissimilarity metrics, and it has been designed to be easily extended with new ones. Through an API, the library exposes a complete functionality to carry out the anomaly detection. Summarizing, to the best of our knowledge, this library is the only one publicly available, that based on dynamic graphs, can be extended with other state-of-the-art anomaly detection techniques. Our experimentation shows promising results regarding the execution times of the algorithms and the accuracy of the implemented techniques. Additionally, the paper provides guidelines for setting the parameters of the detectors to improve their performance and prediction accuracy.