EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection
Qinghua Liu, Seunghak Lee, John Paparrizos
Abstract
Despite the recent focus on time-series anomaly detection, the effectiveness of the proposed anomaly detectors is restricted to specific domains. A model that performs well on one dataset may not perform well on another. Therefore, how to develop automated solutions for anomaly detection for a particular dataset has emerged as a pressing issue. However, there is a noticeable gap in the literature regarding providing a comprehensive review of the ongoing efforts toward automated solutions for selecting or generating scores in an automated manner. Conducting a meta-analysis of proposed methods is challenging due to: (i) their evaluation across limited datasets; (ii) different assumptions on application scenarios; and (iii) the absence of evaluations for out-of-distribution performance. Motivated by the limitations above, we introduce the EasyAD, a modular web engine designed to facilitate the exploration of the first comprehensive benchmark for automated time-series anomaly detection. The EasyAD engine enables rigorous statistical analysis of 20 automated methods and 70 of their variants across the TSB-AD benchmark, a recently curated, heterogeneous dataset spanning nine application domains. The engine supports a two-dimensional evaluation framework, incorporating both accuracy and runtime performance. Our engine allows users to assess the performance of various methods per dataset and per instance, which offers fine-grained analysis per time series. Furthermore, the engine accommodates the processing of user-uploaded data, enabling users to experiment with different model selection strategies on their own datasets.