STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2505.04167v1
• Date: Wed, 07 May 2025 06:41:33 GMT
• Title: STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting
• Authors: Yulong Wang, Xiaofeng Hu, Xiaojian Cui, Kai Wang,
• Abstract summary: STRGCN captures the complex interdependencies in IMTS by representing them as a fully connected graph.<n>Experiments on four public datasets demonstrate that STRGCN achieves state-of-the-art accuracy, competitive memory usage and training speed.
• Score: 14.156419219696252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Irregular multivariate time series (IMTS) are prevalent in real-world applications across many fields, where varying sensor frequencies and asynchronous measurements pose significant modeling challenges. Existing solutions often rely on a pre-alignment strategy to normalize data, which can distort intrinsic patterns and escalate computational and memory demands. Addressing these limitations, we introduce STRGCN, a Spatio-Temporal Relational Graph Convolutional Network that avoids pre-alignment and directly captures the complex interdependencies in IMTS by representing them as a fully connected graph. Each observation is represented as a node, allowing the model to effectively handle misaligned timestamps by mapping all inter-node relationships, thus faithfully preserving the asynchronous nature of the data. Moreover, we enhance this model with a hierarchical ``Sandwich'' structure that strategically aggregates nodes to optimize graph embeddings, reducing computational overhead while maintaining detailed local and global context. Extensive experiments on four public datasets demonstrate that STRGCN achieves state-of-the-art accuracy, competitive memory usage and training speed.

Related papers

• T-Graphormer: Using Transformers for Spatiotemporal Forecasting [2.855856661274715]
  T-Graphormer reduces root mean squared error (RMSE) and mean absolute percentage error (MAPE) by up to 20% and 10%.<n>We show the effectiveness of T-Graphormer on real-world traffic prediction benchmark datasets.
  arXiv  Detail & Related papers  (2025-01-22T23:32:29Z)
• IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs [8.861995276194435]
  This paper addresses domain adaptation challenges in graph data resulting from chronological splits. We propose IMPaCT, a method that imposes invariant properties based on realistic assumptions derived from temporal graph structures. We show that IMPaCT achieves a 3.8% performance improvement over current SOTA method on the ogbn-mag graph dataset.
  arXiv  Detail & Related papers  (2024-11-17T04:23:25Z)
• SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting [19.111041921060366]
  We present a scalable Adaptive Graph Diffusion Forecasting Network (SAGDFN) to capture complex spatial-temporal correlation. SAGDFN is scalable to datasets of thousands of nodes without the need of prior knowledge of spatial correlation. It achieves comparable performance with state-of-the-art baselines on one real-world dataset of 207 nodes and outperforms all state-of-the-art baselines by a significant margin on three real-world datasets of 2000 nodes.
  arXiv  Detail & Related papers  (2024-06-18T05:19:51Z)
• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  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 significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting [0.16492989697868887]
  We introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. We leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks.
  arXiv  Detail & Related papers  (2023-11-21T14:24:21Z)
• Mending of Spatio-Temporal Dependencies in Block Adjacency Matrix [3.529869282529924]
  We propose a novel end-to-end learning architecture designed to mend the temporal dependencies, resulting in a well-connected graph. Our methodology demonstrates superior performance on benchmark datasets, such as SurgVisDom and C2D2.
  arXiv  Detail & Related papers  (2023-10-04T06:42:33Z)
• Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data [50.84488941336865]
  We propose a novel method called Fully- Spatial-Temporal Graph Neural Network (FC-STGNN) For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances. For graph convolution, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations.
  arXiv  Detail & Related papers  (2023-09-11T08:44:07Z)
• 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)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting [12.568905377581647]
  Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. Existing methods cannot accurately model both long-term and short-term temporal correlations simultaneously. We propose a novel spatial-temporal neural network framework, which consists of a graph convolutional recurrent module (GCRN) and a global attention module.
  arXiv  Detail & Related papers  (2023-02-25T03:37:00Z)
• Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting [11.680589359294972]
  We propose TrendGCN to address the challenge of balancing dynamics and robustness. Our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions. Compared with traditional approaches that handle step-wise predictive errors independently, our approach can produce more realistic and robust forecasts.
  arXiv  Detail & Related papers  (2022-08-05T09:36:55Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)

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.