Time series forecasting is a challenging task due to the existence of complex and dynamic temporal dependencies. This can lead to incorrect predictions by even the best forecasting models. Using more training data is one way to improve the accuracy, but this source is often limited. In contrast, we are building on successful denoising approaches for image generation by advocating for an end-to-end forecasting and denoising paradigm.
We propose an end-to-end forecast-blur-denoise forecasting framework by encouraging a division of labors between the forecasting and the denoising models. The initial forecasting model is directed to focus on accurately predicting the coarse-grained behavior, while the denoiser model focuses on capturing the fine-grained behavior that is locally blurred by integrating a Gaussian Process model. All three parts are interacting for the best end-to-end performance. Our extensive experiments demonstrate that our proposed approach is able to improve the forecasting accuracy of several state-of-the-art forecasting models as well as several other denoising approaches.
STeP-Diff: Spatio-Temporal Physics-Informed Diffusion Models for Mobile Fine-Grained Pollution Forecasting
Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Tech- nology (BNRist), Tsinghua University, China（电子工程系，北京信息科学与技术国家研究中心（BNRist），清华大学）；Shenzhen Smartcity Communication, Shenzhen, China（深圳智慧城市通信）；College of Soil and Water Conservation, Southwest Forestry University, China（水土保持学院，西南林业大学）；College of Civil Engineering, Southwest Forestry University, China（土木工程学院，西南林业大学）
From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心、自动化研究所、中国科学院）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室、深圳中国）；School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, China（人工智能学院、中国科学院大学、北京中国）；Wuhan AI Research, Wuhan, China（武汉人工智能研究、武汉中国）；MAPLE Lab, Westlake University（MAPLE实验室、西湖大学）
FlowCast-ODE: Continuous Hourly Weather Forecasting with Dynamic Flow Matching and ODE Solver
ColorfulClouds Technology Co.,Ltd.（ColorfulClouds技术有限公司）；School of Systems Science, Beijing Normal University（北京师范大学系统科学学院）；D-ITET, ETH Zurich（ETH Zurich的D-ITET）
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Amazon Sustainability Science and Innovation（亚马逊可持续性科学与创新部门）
Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
School of Computing and Information Technology（计算与信息科技学院）
