GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection

• URL: http://arxiv.org/abs/2205.11139v1
• Date: Mon, 23 May 2022 08:59:42 GMT
• Title: GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection
• Authors: Xu Chen, Qiu Qiu, Changshan Li, Kunqing Xie
• Abstract summary: Large amount of transaction data raises new challenges for retailers, especially anomaly detection in operating conditions. Traditional time-series anomaly detection methods capture underlying patterns from perspectives of time and attributes, ignoring the difference between retailers in this scenario. We propose GraphAD, a novel multivariate time-series anomaly detection model based on the graph neural network.
• Score: 12.58293845026838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the emergence and development of third-party platforms have greatly facilitated the growth of the Online to Offline (O2O) business. However, the large amount of transaction data raises new challenges for retailers, especially anomaly detection in operating conditions. Thus, platforms begin to develop intelligent business assistants with embedded anomaly detection methods to reduce the management burden on retailers. Traditional time-series anomaly detection methods capture underlying patterns from the perspectives of time and attributes, ignoring the difference between retailers in this scenario. Besides, similar transaction patterns extracted by the platforms can also provide guidance to individual retailers and enrich their available information without privacy issues. In this paper, we pose an entity-wise multivariate time-series anomaly detection problem that considers the time-series of each unique entity. To address this challenge, we propose GraphAD, a novel multivariate time-series anomaly detection model based on the graph neural network. GraphAD decomposes the Key Performance Indicator (KPI) into stable and volatility components and extracts their patterns in terms of attributes, entities and temporal perspectives via graph neural networks. We also construct a real-world entity-wise multivariate time-series dataset from the business data of Ele.me. The experimental results on this dataset show that GraphAD significantly outperforms existing anomaly detection methods.

Related papers

• See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers [23.701716999879636]
  Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data. We introduce a pioneering framework called the Time Series Anomaly Multimodal Analyzer (TAMA) to enhance both the detection and interpretation of anomalies.
  arXiv  Detail & Related papers  (2024-11-04T10:28:41Z)
• Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis [31.43159668073136]
  Unsupervised anomaly detection in time series is essential in industrial applications, as it significantly reduces the need for manual intervention. Traditional methods use Graph Neural Networks (GNNs) or Transformers to analyze spatial while RNNs to model temporal dependencies. This paper introduces a novel temporal model built on an enhanced Graph Attention Network (GAT) for multivariate time series anomaly detection called TopoGDN.
  arXiv  Detail & Related papers  (2024-08-23T14:06:30Z)
• Multitask Active Learning for Graph Anomaly Detection [48.690169078479116]
  We propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE. By coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies. Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection.
  arXiv  Detail & Related papers  (2024-01-24T03:43:45Z)
• 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)
• Low-count Time Series Anomaly Detection [1.3207844222875191]
  Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct challenges surface when modelling low-count time series, particularly low signal-to-noise ratios. We introduce a novel generative procedure for creating benchmark datasets comprising of low-count time series with anomalous segments.
  arXiv  Detail & Related papers  (2023-08-24T16:58:30Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting [0.0]
  We propose DyGraphAD, a time series anomaly detection framework based upon a list of dynamic inter-series graphs. The core idea is to detect anomalies based on the deviation of inter-series relationships and intra-series temporal patterns from normal to anomalous states. Our numerical experiments on real-world datasets demonstrate that DyGraphAD has superior performance than baseline anomaly detection approaches.
  arXiv  Detail & Related papers  (2023-02-04T01:27:01Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach [26.973056364587766]
  Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. We propose a novel framework, graph ANomaly dEtection framework with Multi-scale cONtrastive lEarning (ANEMONE in short) By using a graph neural network as a backbone to encode the information from multiple graph scales (views), we learn better representation for nodes in a graph.
  arXiv  Detail & Related papers  (2022-02-11T09:45:11Z)
• 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)

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.