TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models

• URL: http://arxiv.org/abs/2402.10802v2
• Date: Mon, 26 Feb 2024 14:13:52 GMT
• Title: TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
• Authors: Haotian Si, Changhua Pei, Hang Cui, Jingwen Yang, Yongqian Sun, Shenglin Zhang, Jingjing Li, Haiming Zhang, Jing Han, Dan Pei, Jianhui Li, Gaogang Xie
• Abstract summary: Time series anomaly detection (TSAD) has attracted considerable scholarly and industrial interest. However, existing algorithms exhibit a gap in terms of training paradigm, online detection paradigm, and evaluation criteria. We propose TimeSeriesBench, an industrial-grade benchmark that we continuously maintain as a leaderboard.
• Score: 22.23993481906648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driven by the proliferation of real-world application scenarios and scales, time series anomaly detection (TSAD) has attracted considerable scholarly and industrial interest. However, existing algorithms exhibit a gap in terms of training paradigm, online detection paradigm, and evaluation criteria when compared to the actual needs of real-world industrial systems. Firstly, current algorithms typically train a specific model for each individual time series. In a large-scale online system with tens of thousands of curves, maintaining such a multitude of models is impractical. The performance of using merely one single unified model to detect anomalies remains unknown. Secondly, most TSAD models are trained on the historical part of a time series and are tested on its future segment. In distributed systems, however, there are frequent system deployments and upgrades, with new, previously unseen time series emerging daily. The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown. Lastly, although some papers have conducted detailed surveys, the absence of an online evaluation platform prevents answering questions like "Who is the best at anomaly detection at the current stage?" In this paper, we propose TimeSeriesBench, an industrial-grade benchmark that we continuously maintain as a leaderboard. On this leaderboard, we assess the performance of existing algorithms across more than 168 evaluation settings combining different training and testing paradigms, evaluation metrics and datasets. Through our comprehensive analysis of the results, we provide recommendations for the future design of anomaly detection algorithms. To address known issues with existing public datasets, we release an industrial dataset to the public together with TimeSeriesBench. All code, data, and the online leaderboard have been made publicly available.

Related papers

• Robust Group Anomaly Detection for Quasi-Periodic Network Time Series [47.60720976101336]
  We propose a framework to identify unusual and interesting time series within a network time series database.<n>We develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model.
  arXiv  Detail & Related papers  (2025-06-20T08:11:04Z)
• Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
  Time series data is of great significance in real-world scenarios. Recent years have witnessed remarkable breakthroughs in the time series community. We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• TSI-Bench: Benchmarking Time Series Imputation [52.27004336123575]
  TSI-Bench is a comprehensive benchmark suite for time series imputation utilizing deep learning techniques. The TSI-Bench pipeline standardizes experimental settings to enable fair evaluation of imputation algorithms. TSI-Bench innovatively provides a systematic paradigm to tailor time series forecasting algorithms for imputation purposes.
  arXiv  Detail & Related papers  (2024-06-18T16:07:33Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User [8.05635934199494]
  OrionBench is a continuous benchmarking framework for unsupervised time series anomaly detection models. We show how to use OrionBench, and the performance of pipelines across 17 releases published over the course of four years.
  arXiv  Detail & Related papers  (2023-10-26T19:43:16Z)
• TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis [12.867257563413972]
  Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. We propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD, to exploit both time and frequency domains for performance improvement.
  arXiv  Detail & Related papers  (2022-10-18T09:08:57Z)
• Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors [50.6434162489902]
  We propose a reconstruction-based anomaly detection method, MissGAN, iteratively learning to decode and encode naturally smooth time series. MissGAN does not need labels or only needs labels of normal instances, making it widely applicable.
  arXiv  Detail & Related papers  (2022-04-18T04:34:15Z)
• Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection [40.21502451136054]
  This work presents DGHL, a new family of generative models for time series anomaly detection. A top-down Convolution Network maps a novel hierarchical latent space to time series windows, exploiting temporal dynamics to encode information efficiently. Our method outperformed current state-of-the-art models on four popular benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-15T17:19:44Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection [19.726089445453734]
  Time series anomaly detection is a common but challenging task in many industries. It is difficult to detect anomalies in time series with high accuracy, due to noisy data collected from real world. We propose our anomaly detection model: Time series to Image VAE (T2IVAE)
  arXiv  Detail & Related papers  (2021-01-08T08:35:15Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks [37.16594704493679]
  We propose RobustTAD, a Robust Time series Anomaly Detection framework. It integrates robust seasonal-trend decomposition and convolutional neural network for time series data. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
  arXiv  Detail & Related papers  (2020-02-21T20:43:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.