It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting

• URL: http://arxiv.org/abs/2303.13177v1
• Date: Thu, 23 Mar 2023 11:16:33 GMT
• Title: It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
• Authors: Lars {\O}degaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
• Abstract summary: We propose a framework for learning temporal and spatial dependencies using Graph Neural Network (GNN) networks. GNNs represent every sample as its own node in a graph, rather than all measurements for a particular location as a single node. The framework does not require measurements along the temporal dimension, meaning that it also facilitates irregular time series, different frequencies or missing data, without the need for data sampling imputation.
• Score: 1.278093617645299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinct components that learn spatial and temporal dependencies. A common methodology employs some Graph Neural Network (GNN) to capture relations between spatial locations, while another network, such as a recurrent neural network (RNN), learns temporal correlations. By representing every recorded sample as its own node in a graph, rather than all measurements for a particular location as a single node, temporal and spatial information is encoded in a similar manner. In this setting, GNNs can now directly learn both temporal and spatial dependencies, jointly, while also alleviating the need for additional temporal networks. Furthermore, the framework does not require aligned measurements along the temporal dimension, meaning that it also naturally facilitates irregular time series, different sampling frequencies or missing data, without the need for data imputation. To evaluate the proposed methodology, we consider wind speed forecasting as a case study, where our proposed framework outperformed other spatio-temporal models using GNNs with either Transformer or LSTM networks as temporal update functions.

Related papers

• 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)
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection [98.41798478488101]
  Time series analytics is crucial to unlocking the wealth of information implicit in available data. Recent advancements in graph neural networks (GNNs) have led to a surge in GNN-based approaches for time series analysis. This survey brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.
  arXiv  Detail & Related papers  (2023-07-07T08:05:03Z)
• 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)
• Scalable Spatiotemporal Graph Neural Networks [14.415967477487692]
  Graph neural networks (GNNs) are often the core component of the forecasting architecture. In most pretemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph. We propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics.
  arXiv  Detail & Related papers  (2022-09-14T09:47:38Z)
• 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)
• Space-Time Graph Neural Networks [104.55175325870195]
  We introduce space-time graph neural network (ST-GNN) to jointly process the underlying space-time topology of time-varying network data. Our analysis shows that small variations in the network topology and time evolution of a system does not significantly affect the performance of ST-GNNs.
  arXiv  Detail & Related papers  (2021-10-06T16:08:44Z)
• Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging [3.4386226615580107]
  SATCN is a universal and flexible framework to performtemporal kriging for various datasets without need for model specification. We capture nodes by temporal convolutional networks, which allows our model to cope with data of diverse sizes. We conduct extensive experiments on three real-world datasets, including traffic and climate recordings.
  arXiv  Detail & Related papers  (2021-09-24T18:43:07Z)
• Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting [11.495992519252585]
  We argue that such correlations are universal and play a pivotal role in traffic flow. We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor. The proposed STNN model can be applied on any unseen traffic networks.
  arXiv  Detail & Related papers  (2021-09-11T09:04:35Z)
• SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network [2.524966118517392]
  We have designed a simplified S-temporal GNN(SST-GNN) that effectively encodes the dependency by separately aggregating different neighborhood. We have shown that our model has significantly outperformed the state-of-the-art models on three real-world traffic datasets.
  arXiv  Detail & Related papers  (2021-03-31T18:28:44Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

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.