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
- Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly Detection in Dynamic Graphs [52.956235109354175]
Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes.
We introduce a novel Spatial-Temporal memories-enhanced graph autoencoder (STRIPE)
STRIPE has demonstrated a superior capability to discern anomalies by effectively leveraging the distinct spatial and temporal dynamics of dynamic graphs.
arXiv Detail & Related papers (2024-03-14T02:26:10Z) - 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) - Log-based Anomaly Detection of Enterprise Software: An Empirical Study [0.0]
We evaluate several state-of-the-art anomaly detection models on an industrial dataset from our research partner.
Results show that while all models are capable of detecting anomalies, certain models are better suited for less-structured datasets.
arXiv Detail & Related papers (2023-10-31T14:32:08Z) - 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) - 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 [49.439021563395976]
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) - Federated Variational Learning for Anomaly Detection in Multivariate
Time Series [13.328883578980237]
We propose an unsupervised time series anomaly detection framework in a federated fashion.
We leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model.
Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models.
arXiv Detail & Related papers (2021-08-18T22:23:15Z) - 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)
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.