Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data

• URL: http://arxiv.org/abs/2202.05093v1
• Date: Thu, 10 Feb 2022 15:32:38 GMT
• Title: Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data
• Authors: Kyeong-Joong Jeong, Jin-Duk Park, Kyusoon Hwang, Seong-Lyun Kim, Won-Yong Shin
• Abstract summary: We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line. In Stage I, we select anomaly candidates by using a model trained by operation cycle signals; in Stage II, we finally detect abnormal events out of the candidates. A distinguishable feature of our framework is that operation cycle signals are exploited first to find likely anomalous points, whereas sensor signals are leveraged to filter out unlikely anomalous points afterward.
• Score: 3.43862266155801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line. Given heterogeneous time series data consisting of operation cycle signals and sensor signals, we aim at discovering abnormal events. Motivated by our empirical findings that conventional single-stage benchmark approaches may not exhibit satisfactory performance under our challenging circumstances, we propose a two-stage deep anomaly detection (TDAD) framework in which two different unsupervised learning models are adopted depending on types of signals. In Stage I, we select anomaly candidates by using a model trained by operation cycle signals; in Stage II, we finally detect abnormal events out of the candidates by using another model, which is suitable for taking advantage of temporal continuity, trained by sensor signals. A distinguishable feature of our framework is that operation cycle signals are exploited first to find likely anomalous points, whereas sensor signals are leveraged to filter out unlikely anomalous points afterward. Our experiments comprehensively demonstrate the superiority over single-stage benchmark approaches, the model-agnostic property, and the robustness to difficult situations.

Related papers

• Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders [16.31103717602631]
  Time series anomaly detection plays a vital role in a wide range of applications. Existing methods require training one specific model for each dataset. We propose a general time series anomaly detection model, which is pre-trained on extensive multi-domain datasets.
  arXiv  Detail & Related papers  (2024-05-24T06:59:43Z)
• 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)
• Time series anomaly detection with reconstruction-based state-space models [10.085100442558828]
  We propose a novel unsupervised anomaly detection method for time series data. A long short-term memory (LSTM)-based encoder-decoder is adopted to represent the mapping between the observation space and the latent space. Regularization of the latent space places constraints on the states of normal samples, and Mahalanobis distance is used to evaluate the abnormality level.
  arXiv  Detail & Related papers  (2023-03-06T17:52:35Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals [10.866594993485226]
  We propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M) We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bigressive LSTM with Attention) to capture temporal dependence from time-series data.
  arXiv  Detail & Related papers  (2021-07-27T06:48:20Z)
• Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal [63.18666008322476]
  Sleep problems are one of the major diseases all over the world. Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. Specialists have to score the different signals according to one of the standard guidelines.
  arXiv  Detail & Related papers  (2021-03-30T09:59:56Z)
• Dual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection [0.0]
  We develop a dual-encoder in a bidirectional GAN architecture that is trained simultaneously with a generator and a discriminator network. We show that our proposed method performs well in capturing the distribution of normal samples, thereby improving anomaly detection on GAN-based models.
  arXiv  Detail & Related papers  (2020-12-22T05:05:33Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• 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)
• A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video [120.18562044084678]
  Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. We propose a background-agnostic framework that learns from training videos containing only normal events.
  arXiv  Detail & Related papers  (2020-08-27T18:39:24Z)
• Sequential Adversarial Anomaly Detection for One-Class Event Data [18.577418448786634]
  We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available. We propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator.
  arXiv  Detail & Related papers  (2019-10-21T06:12:47Z)

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.