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Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting.
Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In IJCAI 2019: 28th International Joint Conference on Artificial Intelligence . 1907–1913
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 1913
Earlier work this paper cites.
Scoring rules for continuous probability distributions
James E Matheson and Robert L Winkler. 1976 · 1976
Earlier work this paper cites.
Interpolation methods for spatio-temporal geographic data
Lixin Li and Peter Revesz. 2004 · 2004
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan. 2005 · 2005
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent. 2011 · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning . PMLR, 1278–1286
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
WaveNet: A Generative Model for Raw Audio. In 9th ISCA Speech Synthesis Workshop . 125–125
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
Concrete dropout
Yarin Gal, Jiri Hron, and Alex Kendall. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018 · 2018
Earlier work this paper cites.
Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018 · 2018
Earlier work this paper cites.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018 · 2018
Earlier work this paper cites.
Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 32
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018 · 2018
Earlier work this paper cites.
Deep distributed fusion network for air quality prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 965–973
Xiuwen Yi, Junbo Zhang, Zhaoyuan Wang, Tianrui Li, and Yu Zheng. 2018 · 2018
Cited alongside, same era.
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In IJCAI 2018: 27th International Joint Conference on Artificial Intelligence . 3634–3640
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018 · 2018
Cited alongside, same era.
Latent ordinary differential equations for irregularly-sampled time series
Yulia Rubanova, Ricky TQ Chen, and David K Duvenaud. 2019 · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon. 2019 · 2019
Cited alongside, same era.
WaveGrad: Estimating Gradients for Waveform Generation. In International Conference on Learning Representations
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan. 2020 · 2020
RNN with particle flow for probabilistic spatio-temporal forecasting. In International Conference on Machine Learning . PMLR, 8336–8348
Soumyasundar Pal, Liheng Ma, Yingxue Zhang, and Mark Coates. 2021 · 2021
Later among the works it cites.
Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference on Machine Learning . PMLR, 8857–8868
Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf. 2021 · 2021
Later among the works it cites.
Spatio-temporal graph neural networks for multi-site PV power forecasting
Jelena Simeunović, Baptiste Schubnel, Pierre-Jean Alet, and Rafael E Carrillo. 2021 · 2021
Later among the works it cites.
CSDI: Conditional score-based diffusion models for probabilistic time series imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. 2021 · 2021
Later among the works it cites.
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1841–1851
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Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
Glow-tts: A generative flow for text-to-speech via monotonic alignment search
Jaehyeon Kim, Sungwon Kim, Jungil Kong, and Sungroh Yoon. 2020 · 2020
Cited alongside, same era.
DiffWave: A Versatile Diffusion Model for Audio Synthesis. In International Conference on Learning Representations
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2020 · 2020
Cited alongside, same era.
Permutation invariant graph generation via score-based generative modeling. In International Conference on Artificial Intelligence and Statistics . PMLR, 4474–4484
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. 2020 · 2020
Cited alongside, same era.
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Hao Peng, Hongfei Wang, Bowen Du, Md Zakirul Alam Bhuiyan, Hongyuan Ma, Jianwei Liu, Lihong Wang, Zeyu Yang, Linfeng Du, Senzhang Wang, et al · 2020
Cited alongside, same era.
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. 2020 · 2020
Cited alongside, same era.
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. 2020 · 2020
Cited alongside, same era.
Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, and Rose Yu. 2021 · 2021
Later among the works it cites.
Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 11106–11115
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021 · 2021
Later among the works it cites.
Graph neural controlled differential equations for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 6367–6374
Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, and Noseong Park. 2022 · 2022
Later among the works it cites.
STDEN: Towards physics-guided neural networks for traffic flow prediction. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 4048–4056
Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang. 2022 · 2022
Later among the works it cites.
AirFormer: Predicting Nationwide Air Quality in China with Transformers
Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, and Roger Zimmermann. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
MCVD-Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation. In Advances in Neural Information Processing Systems
Vikram Voleti, Alexia Jolicoeur-Martineau, and Christopher Pal. 2022 · 2022
Later among the works it cites.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning . PMLR, 27268–27286
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022 · 2022
Later among the works it cites.
TDSTF: Transformer-based Diffusion probabilistic model for Sparse Time series Forecasting
Ping Chang, Huayu Li, Stuart F Quan, Janet Roveda, and Ao Li. 2023 · 2023
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Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
Yan Li, Xinjiang Lu, Yaqing Wang, and Dejing Dou. 2023 · 2023
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DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model
Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, and Yi Wang. 2023 · 2023
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Transformers in time series: A survey. In International Joint Conference on Artificial Intelligence (IJCAI)
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. 2023 · 2023
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