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GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention

arXiv 2104.05914 ↗First posted 13 Apr 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

Forecasting graph-based, time-dependent data has broad practical applications but presents challenges. Effective models must capture both spatial and temporal dependencies in the data, while also incorporating auxiliary information to enhance prediction accuracy. In this paper, we identify limitations in current state-of-the-art models regarding temporal dependency handling. To overcome this, we introduce GSA-Forecaster, a new deep learning model designed for forecasting in graph-based, time-dependent contexts. GSA-Forecaster utilizes graph sequence attention, a new attention mechanism proposed in this paper, to effectively manage temporal dependencies. GSA-Forecaster integrates the data's graph structure directly into its architecture, addressing spatial dependencies. Additionally, it incorporates auxiliary information to refine its predictions further. We validate its performance using real-world graph-based, time-dependent datasets, where it demonstrates superior effectiveness compared to existing state-of-the-art models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 70 references that are in the map. First posted on arXiv 13 Apr 2021.

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34 of 70 references.

References · 34

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2017Graph Attention Networkscited by 8,854
  6. 2020End-to-End Object Detection with Transformerscited by 7,384
  7. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  8. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  9. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  10. 2017Non-local Neural Networkscited by 2,290
  11. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  12. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,830
22 more
  1. 2019Generating Long Sequences with Sparse Transformerscited by 1,523
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  3. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  4. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  5. 2018Image Transformercited by 770
  6. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  7. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  8. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  9. 2019Graph Transformer Networkscited by 520
  10. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  11. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  12. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  13. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  14. 2015Structural-RNN: Deep Learning on Spatio-Temporal Graphscited by 234
  15. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  16. 2019Text Generation from Knowledge Graphs with Graph Transformerscited by 122
  17. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 119
  18. 2015Signal Processing on Graphs: Causal Modeling of Unstructured Datacited by 27
  19. 2019Path-Augmented Graph Transformer Networkcited by 22
  20. 2018Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discoverycited by 16
  21. 2019Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Datacited by 11
  22. 2015An Introduction to Temporal Graphs: An Algorithmic Perspectivecited by 10

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