Fetching the paper…
Reading the bibliography…
Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains.
Fast graph representation learning with PyTorch Geometric
Fey, M.; and Lenssen, J. E. 2019 · 1903
Earlier work this paper cites.
Graph Wavenet for Deep Spatial-Temporal Graph Modeling
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C. 2019 · 1913
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014 · 1958
Earlier work this paper cites.
Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
The “echo state” approach to analysing and training recurrent neural networks-with an erratum note
Jaeger, H. 2001 · 2001
Earlier work this paper cites.
Optimization and Applications of Echo State Networks with Leaky-Integrator Neurons
Jaeger, H.; Lukoševičius, M.; Popovici, D.; and Siewert, U. 2007 · 2007
Earlier work this paper cites.
Correntropy: Properties and applications in non-Gaussian signal processing
Liu, W.; Pokharel, P. P.; and Principe, J. C. 2007 · 2007
Earlier work this paper cites.
The graph neural network model
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2008 · 2008
Earlier work this paper cites.
Reservoir Computing Approaches to Recurrent Neural Network Training
Lukoševičius, M.; and Jaeger, H. 2009 · 2009
Earlier work this paper cites.
Python 3 Reference Manual
Van Rossum, G.; and Drake, F. L. 2009 · 2009
Earlier work this paper cites.
CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010 [dataset]
Commission for Energy Regulation. 2016 · 2010
Earlier work this paper cites.
Sub-hour solar data for power system modeling from static spatial variability analysis
Hummon, M.; Ibanez, E.; Brinkman, G.; and Lew, D. 2012 · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
A practical guide to applying echo state networks
Lukoševičius, M. 2012 · 2012
Earlier work this paper cites.
Big data and its technical challenges
Jagadish, H. V.; Gehrke, J.; Labrinidis, A.; Papakonstantinou, Y.; Patel, J. M.; Ramakrishnan, R.; and Shahabi, C. 2014 · 2014
Earlier work this paper cites.
Srivastava, R. K.; Greff, K.; and Schmidhuber, J. 2015 · 2015
Earlier work this paper cites.
Hendrycks, D.; and Gimpel, K. 2016 · 2016
Cited alongside, same era.
Deep reservoir computing: A critical experimental analysis
Gallicchio, C.; Micheli, A.; and Pedrelli, L. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Battaglia, P. W.; Hamrick, J. B.; Bapst, V.; Sanchez-Gonzalez, A.; Zambaldi, V.; Malinowski, M.; Tacchetti, A.; Raposo, D.; Santoro, A.; Faulkner, R.; et al. 2018 · 2018
Cited alongside, same era.
SIGN: Scalable Inception Graph Neural Networks
Frasca, F.; Rossi, E.; Eynard, D.; Chamberlain, B.; Bronstein, M.; and Monti, F. 2020 · 2020
Later among the works it cites.
Array programming with NumPy
Harris, C. R.; Millman, K. J.; van der Walt, S. J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N. J.; et al. 2020 · 2020
Later among the works it cites.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; Chang, X.; and Zhang, C. 2020 · 2020
Later among the works it cites.
Gman: A graph multi-attention network for traffic prediction
Zheng, C.; Fan, X.; Wang, C.; and Qi, J. 2020 · 2020
Later among the works it cites.
Spatio-Temporal Multi-graph Networks for Demand Forecasting in Online Marketplaces
Gandhi, A.; Kaveri, S.; Chaoji, V.; et al. 2021 · 2021
Later among the works it cites.
Neptune: Metadata store for MLOps, built for research and production teams that run a lot of experiments
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Design of deep echo state networks
Gallicchio, C.; Micheli, A.; and Pedrelli, L. 2018 · 2018
Cited alongside, same era.
Modeling long-and short-term temporal patterns with deep neural networks
Lai, G.; Chang, W.-C.; Yang, Y.; and Liu, H. 2018 · 2018
Cited alongside, same era.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Li, Y.; Yu, R.; Shahabi, C.; and Liu, Y. 2018 · 2018
Cited alongside, same era.
Structured sequence modeling with graph convolutional recurrent networks
Seo, Y.; Defferrard, M.; Vandergheynst, P.; and Bresson, X. 2018 · 2018
Cited alongside, same era.
Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
Yu, B.; Yin, H.; and Zhu, Z. 2018 · 2018
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L.; Liu, X.; Si, S.; Li, Y.; Bengio, S.; and Hsieh, C.-J. 2019 · 2019
Cited alongside, same era.
PyTorch Lightning
Falcon, W.; and The PyTorch Lightning team. 2019 · 2019
Cited alongside, same era.
neptune.ai. 2021 · 2021
Later among the works it cites.
FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting
Oreshkin, B. N.; Amini, A.; Coyle, L.; and Coates, M. 2021 · 2021
Later among the works it cites.
Inductive graph neural networks for spatiotemporal kriging
Wu, Y.; Zhuang, D.; Labbe, A.; and Sun, L. 2021 · 2021
Later among the works it cites.
Torch Spatiotemporal
Cini, A.; and Marisca, I. 2022 · 2022
Closest in time.
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
Cini, A.; Marisca, I.; and Alippi, C. 2022 · 2022
Closest in time.
On the Equivalence Between Temporal and Static Equivariant Graph Representations
Gao, J.; and Ribeiro, B. 2022 · 2022
Closest in time.
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
Marisca, I.; Cini, A.; and Alippi, C. 2022 · 2022
Closest in time.
Discrete-time dynamic graph echo state networks
Micheli, A.; and Tortorella, D. 2022 · 2022
Closest in time.
Multivariate Time Series Forecasting with Latent Graph Inference
Satorras, V. G.; Rangapuram, S. S.; and Januschowski, T. 2022 · 2022
Closest in time.
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
Wu, Z.; Zheng, D.; Pan, S.; Gan, Q.; Long, G.; and Karypis, G. 2022 · 2022
Closest in time.