TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

TL;DR

This paper introduces TAB, a comprehensive benchmark with diverse datasets and a unified evaluation pipeline, to facilitate fair comparison and assessment of various time series anomaly detection methods.

Contribution

The paper presents TAB, a new benchmark with extensive datasets and an automated evaluation framework for fair comparison of TSAD methods.

Findings

TAB enables comprehensive evaluation across diverse datasets

It provides insights into the performance of different TSAD methods

The benchmark promotes standardized assessment in TSAD research

Abstract

Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time series data will be available, leading also to growing demands for TSAD. While many TSAD methods already exist, new and better methods are still desirable. However, effective progress hinges on the availability of reliable means of evaluating new methods and comparing them with existing methods. We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods. Besides, all datasets and code are available at https://github.com/decisionintelligence/TAB.