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Spatio-temporal forecasting is pivotal in numerous real-world applications, including transportation planning, energy management, and climate monitoring.
Hamiltonian systems and transformation in hilbert space
Bernard O Koopman · 1931
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Dynamic mode decomposition of numerical and experimental data
Peter J Schmid · 2010
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Variants of dynamic mode decomposition: boundary condition, koopman, and fourier analyses
Kevin K Chen, Jonathan H Tu, and Clarence W Rowley · 2012
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Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
Bingni W Brunton, Lise A Johnson, Jeffrey G Ojemann, and J Nathan Kutz · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Deep spatio-temporal residual networks for citywide crowd flows prediction
Junbo Zhang, Yu Zheng, and Dekang Qi · 2017
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Modeling long-and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2018
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Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting
Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu · 2019
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Graph wavenet for deep spatial-temporal graph modeling
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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Language models are few-shot learners
Tom B Brown · 2020
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Stgcn: a spatial-temporal aware graph learning method for poi recommendation
Haoyu Han, Mengdi Zhang, Min Hou, Fuzheng Zhang, Zhongyuan Wang, Enhong Chen, Hongwei Wang, Jianhui Ma, and Qi Liu · 2020
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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
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Connecting the dots: Multivariate time series forecasting with graph neural networks
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang · 2020
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Historical inertia: A neglected but powerful baseline for long sequence time-series forecasting
Yue Cui, Jiandong Xie, and Kai Zheng · 2021
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St-norm: Spatial and temporal normalization for multi-variate time series forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, and Ivor W Tsang · 2021
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Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, and Gao Cong · 2021
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Joint air quality and weather prediction based on multi-adversarial spatiotemporal networks
Jindong Han, Hao Liu, Hengshu Zhu, Hui Xiong, and Dejing Dou · 2021
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Reversible instance normalization for accurate time-series forecasting against distribution shift
Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo · 2021
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Voice2series: Reprogramming acoustic models for time series classification
Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
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Stden: Towards physics-guided neural networks for traffic flow prediction
Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang · 2022
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2022
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Test: Text prototype aligned embedding to activate llm’s ability for time series
Chenxi Sun, Yaliang Li, Hongyan Li, and Shenda Hong · 2023
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Llama: Open and efficient foundation language models. corr, abs/2302.13971, 2023. doi: 10.48550
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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When urban region profiling meets large language models
Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang · 2023
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Vijay Ekambaram, Arindam Jati, Nam H Nguyen, Pankaj Dayama, Chandra Reddy, Wesley M Gifford, and Jayant Kalagnanam · 2024
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Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting
Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, and Yongjun Xu · 2022
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Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen · 2022
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Tempo: Prompt-based generative pre-trained transformer for time series forecasting
Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu · 2023
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A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou · 2023
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson · 2023
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Language models represent space and time
Wes Gurnee and Max Tegmark · 2023
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Machine learning for urban air quality analytics: A survey
Jindong Han, Weijia Zhang, Hao Liu, and Hui Xiong · 2023
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson · 2024
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Airphynet: Harnessing physics-guided neural networks for air quality prediction
Kethmi Hirushini Hettige, Jiahao Ji, Shili Xiang, Cheng Long, Gao Cong, and Jingyuan Wang · 2024
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Position paper: What can large language models tell us about time series analysis
Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen · 2024
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Urbangpt: Spatio-temporal large language models
Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, and Chao Huang · 2024
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Opencity: Open spatio-temporal foundation models for traffic prediction
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Spatial-temporal large language model for traffic prediction
Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, and Rui Zhao · 2024
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How can large language models understand spatial-temporal data?
Lei Liu, Shuo Yu, Runze Wang, Zhenxun Ma, and Yanming Shen · 2024
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Koopa: Learning non-stationary time series dynamics with koopman predictors
Yong Liu, Chenyu Li, Jianmin Wang, and Mingsheng Long · 2024
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Autotimes: Autoregressive time series forecasters via large language models
Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, and Mingsheng Long · 2024
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Are language models actually useful for time series forecasting?
Mingtian Tan, Mike A Merrill, Vinayak Gupta, Tim Althoff, and Thomas Hartvigsen · 2024
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Fouriergnn: Rethinking multivariate time series forecasting from a pure graph perspective
Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbing Cao, and Zhendong Niu · 2024
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Analysis of global and key pm2. 5 dynamic mode decomposition based on the koopman method
Yuhan Yu, Dantong Liu, Bin Wang, and Feng Zhang · 2024
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Unist: a prompt-empowered universal model for urban spatio-temporal prediction
Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, and Yong Li · 2024
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One fits all: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al · 2024
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