Theseus

Abstract

The detection of anomalies in time series has gained ample academic and industrial attention, yet, no comprehensive benchmark exists to evaluate time-series anomaly detection methods. Therefore, there is no final verdict on which method performs the best (and under what conditions). Consequently, we often observe methods performing exceptionally well on one dataset but surprisingly poorly on another, creating an illusion of progress. To address these issues, we thoroughly studied over one hundred papers, and summarized our effort in TSB-UAD, a new benchmark to evaluate univariate time series anomaly detection methods. In this paper, we describe Theseus, a modular and extensible web application that helps users navigate through the benchmark, and reason about the merits and drawbacks of both anomaly detection methods and accuracy measures, under different conditions. Overall, our system enables users to compare 12 anomaly detection methods on 1980 time series, using 13 accuracy measures, and decide on the most suitable method and measure for some application.

An Interactive Dive into Time-Series Anomaly Detection

“…3. Example of interactive systems that (a,b,d) allow the user to dive into computational steps [58], [59], [60], or (c,e) experimental results [61], [62].…”

“…This is becoming possible by recently proposed prototype systems [59], [60], which enable users to interactively explore methods and their inner-workings, and thus, better understand the different computation steps. In this tutorial, we will discuss and demonstrate recently proposed systems for anomaly detection [58], [60], [59], as well as systems that allow the user to navigate and explore large experimental evaluation studies [62], [61]. Figure 3 illustrates examples of these systems.…”

dCNN/dCAM: anomaly precursors discovery in multivariate time series with deep convolutional neural networks

“…Massive collections of data series (or time series) are becoming a reality in virtually every scientific and social domain, and there is an increasingly pressing need for relevant applications to develop techniques that can efficiently analyze them (Palpanas, 2015;Bagnall et al, 2019;Wang and Palpanas, 2021;Jakovljevic et al, 2022). Data series anomaly detection is a crucial problem with applications in a wide range of fields (Palpanas, 2015;Boniol et al, 2022;Paparrizos et al, 2022aPaparrizos et al, , 2022b, that all share the same well-studied goal (Barnet and Lewis, 1994;Subramaniam et al, 2006;Yeh et al, 2016): detecting anomalies as fast as possible to avoid any critical event. Such applications can be found in biology, astronomy, and engineering areas.…”