Learning Graph Structures with Transformer for Multivariate Time Series
Anomaly Detection in IoT
- URL: http://arxiv.org/abs/2104.03466v1
- Date: Thu, 8 Apr 2021 01:45:28 GMT
- Title: Learning Graph Structures with Transformer for Multivariate Time Series
Anomaly Detection in IoT
- Authors: Zekai Chen, Dingshuo Chen, Zixuan Yuan, Xiuzhen Cheng, Xiao Zhang
- Abstract summary: This work proposed a novel framework, namely GTA, for multivariate time series anomaly detection by automatically learning a graph structure followed by the graph convolution.
We also devised a novel graph convolution named Influence propagation convolution to model the anomaly information flow between graph nodes.
The experiments on four public anomaly detection benchmarks further demonstrate our approach's superiority over other state-of-the-arts.
- Score: 11.480824844205864
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many real-world IoT systems comprising various internet-connected sensory
devices generate substantial amounts of multivariate time series data.
Meanwhile, those critical IoT infrastructures, such as smart power grids and
water distribution networks, are often targets of cyber-attacks, making anomaly
detection of high research value. However, considering the complex topological
and nonlinear dependencies that are initially unknown among sensors, modeling
such relatedness is inevitable for any efficient and accurate anomaly detection
system. Additionally, due to multivariate time series' temporal dependency and
stochasticity, their anomaly detection remains a big challenge. This work
proposed a novel framework, namely GTA, for multivariate time series anomaly
detection by automatically learning a graph structure followed by the graph
convolution and modeling the temporal dependency through a Transformer-based
architecture. The core idea of learning graph structure is called the
connection learning policy based on the Gumbel-softmax sampling strategy to
learn bi-directed associations among sensors directly. We also devised a novel
graph convolution named Influence Propagation convolution to model the anomaly
information flow between graph nodes. Moreover, we proposed a multi-branch
attention mechanism to substitute for original multi-head self-attention to
overcome the quadratic complexity challenge. The extensive experiments on four
public anomaly detection benchmarks further demonstrate our approach's
superiority over other state-of-the-arts.
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) - 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) - Coupled Attention Networks for Multivariate Time Series Anomaly
Detection [10.620044922371177]
We propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data.
To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module.
arXiv Detail & Related papers (2023-06-12T13:42:56Z) - HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly
Detection [2.253268952202213]
We propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS.
We first combine the embedding similarity subgraph generated by sensor embedding and feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph.
This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning.
arXiv Detail & Related papers (2022-11-01T05:01:34Z) - Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data.
We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism.
We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
arXiv Detail & Related papers (2022-06-30T06:00:13Z) - 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) - 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) - HIFI: Anomaly Detection for Multivariate Time Series with High-order
Feature Interactions [7.016615391171876]
HIFI builds multivariate feature interaction graph automatically and uses the graph convolutional neural network to achieve high-order feature interactions.
Experiments on three publicly available datasets demonstrate the superiority of our framework compared with state-of-the-art approaches.
arXiv Detail & Related papers (2021-06-11T04:57:03Z) - 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.