mlsanityData to 7 Oct 2026

A Study of Joint Graph Inference and Forecasting

arXiv 2109.04979 ↗First posted 10 Sep 2021cs.LGcited by 5

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Abstract (arXiv)

We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series. The core assumption behind these models is that there is a latent graph between the time series (nodes) that governs the evolution of the multivariate time series. By parameterizing a graph in a differentiable way, the models aim to improve forecasting quality. We compare four recent models of this class on the forecasting task. Further, we perform ablations to study their behavior under changing conditions, e.g., when disabling the graph-learning modules and providing the ground-truth relations instead. Based on our findings, we propose novel ways of combining the existing architectures.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 10 Sep 2021.

Builds on

18 of 29 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  6. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  7. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  8. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  9. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  10. 2018Neural Relational Inference for Interacting Systemscited by 339
  11. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  12. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
6 more
  1. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  2. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  3. 2018NetGAN: Generating Graphs via Random Walkscited by 129
  4. 2020A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecastingcited by 54
  5. 2018GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Predictioncited by 47
  6. 2018Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Datacited by 10

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Cited by year

Citing papers per year: 2022: 3; 2023: 2.

5 papers.

  1. 2022Sparse Graph Learning from Spatiotemporal Time Seriescited by 22
  2. 2022Multivariate Time Series Forecasting with Latent Graph Inferencecited by 20
  3. 2023DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systemscited by 10
  4. 2023GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecastingcited by 1
  5. 2022Balanced Graph Structure Learning for Multivariate Time Series Forecastingcited by 0