Spatiotemporal Forecasting

Spatiotemporal forecasting aims to predict future values of data exhibiting both spatial and temporal dependencies, crucial for applications like weather prediction, traffic management, and energy forecasting. Current research emphasizes improving model efficiency and accuracy using deep learning architectures such as Graph Neural Networks (GNNs), Transformers, and recurrent networks (RNNs), often incorporating techniques like attention mechanisms, vector quantization, and knowledge distillation to handle high-dimensional data and complex relationships. These advancements are driving improvements in prediction accuracy and enabling the integration of diverse data sources, including numerical and visual data, leading to more reliable and informative forecasts across various domains. The resulting improvements have significant implications for resource management, risk mitigation, and decision-making in numerous sectors.

Papers

September 27, 2024

HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting
Shuowei Cai, Hao Liu
Temporal Dependency Heterogeneous Federated Learning Spatiotemporal Forecasting Time sEries

September 23, 2024

Automated Spatio-Temporal Weather Modeling for Load Forecasting
Julie Keisler (CRIStAL, EDF R\&D OSIRIS, EDF R\&D), Margaux Bregere (EDF R\&D, EDF R\&D OSIRIS, LPSM (UMR\_8001))
Load Forecasting Spatiotemporal Forecasting Renewable Generation Weather Prediction Model

August 26, 2024

Efficient fine-tuning of 37-level GraphCast with the Canadian global deterministic analysis
Christopher Subich
Data Driven Forecasting Performance Global Model Efficient Fine Tuning Spatiotemporal Forecasting

May 29, 2024

May 17, 2024

Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, Xuan Song
Meta Learning Spatiotemporal Forecasting Spatiotemporal Feature Spatiotemporal Learning Spatiotemporal Network

May 10, 2024

Enhancing Traffic Prediction with Textual Data Using Large Language Models
Xiannan Huang
Traffic Prediction Traffic Data Spatiotemporal Forecasting Short Term Traffic

March 25, 2024

VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting
Yujin Tang, Peijie Dong, Zhenheng Tang, Xiaowen Chu, Junwei Liang
High Efficiency LSTM Network Traditional RNNs Vision Mamba Spatiotemporal Forecasting Spatiotemporal Prediction

February 23, 2024

February 16, 2024

Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi
Missing Data Spatiotemporal Forecasting Spatiotemporal Graph Spatiotemporal Dynamic Graph Forecasting Spatio Temporal Downsampling

January 26, 2024

VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting
Yutong Xiong, Xun Zhu, Ming Wu, Weiqing Li, Fanbin Mo, Chuang Zhang, Bin Zhang
Fine Grained Many Sparse Spatiotemporal Forecasting W Net Visibility Estimation

December 10, 2023

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting
Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, Ursula Eicker
Graph Neural Network Spatiotemporal Forecasting Streamflow Prediction

December 6, 2023

SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting
Chao Chen, Tian Zhou, Yanjun Zhao, Hui Liu, Liang Sun, Rong Jin
Vector Quantization Spatio Temporal Forecasting Sparse Regression Spatiotemporal Forecasting Temporal Benchmark

December 1, 2023

Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, Xuan Song
Spatiotemporal Forecasting Time sEries Spatiotemporal Fluid Process

July 19, 2023

PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle Maddix, Yi Zhu, Mu Li, Yuyang Wang
Latent Diffusion Model Probabilistic Forecast Earth System SpatioTemporal Data Precipitation Nowcasting Spatiotemporal Forecasting

June 3, 2023

DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
Salva Rühling Cachay, Bo Zhao, Hailey Joren, Rose Yu
Diffusion Model Probabilistic Forecast Spatiotemporal Forecasting Diffusion Dynamic Gaussian Diffusion

February 28, 2023

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms
Sunghcul Hong, Yunjin Choi, Jong-June Jeon
Neural Network Model Spatiotemporal Forecasting Causal Network Complex Predictive Model

February 22, 2023

Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts
Pantelis R. Vlachas, Petros Koumoutsakos
LeArning Abstract Training Data Human Prediction Recurrent Neural Network Autoregressive Model Convolutional Autoencoder Spatiotemporal Forecasting Backpropagation Through Time

January 31, 2023

3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations
Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junichiro Makino
Deep Learning Prompt Expansion Spatiotemporal Forecasting Supernova Affected Particle

Spatiotemporal Forecasting

Papers

HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting

Automated Spatio-Temporal Weather Modeling for Load Forecasting

Efficient fine-tuning of 37-level GraphCast with the Canadian global deterministic analysis

Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks

SFANet: Spatial-Frequency Attention Network for Weather Forecasting

Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting

Enhancing Traffic Prediction with Textual Data Using Large Language Models

VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization

Spatiotemporal Observer Design for Predictive Learning of High-Dimensional Data

Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling

VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting

Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts

3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations