Forecasting multivariate time series is a computationally intensive task challenged by extreme or redundant samples. Recent resampling methods aim to increase training efficiency by reweighting samples based on their running losses. However, these methods do not solve the problems caused by heavy-tailed distribution losses, such as overfitting to outliers. To tackle these issues, we introduce a novel approach: a Gaussian loss-weighted sampler that multiplies their running losses with a Gaussian distribution weight. It reduces the probability of selecting samples with very low or very high losses while favoring those close to average losses. As it creates a weighted loss distribution that is not heavy-tailed theoretically, there are several advantages to highlight compared to existing methods: 1) it relieves the inefficiency in learning redundant easy samples and overfitting to outliers, 2) It improves training efficiency by preferentially learning samples close to the average loss. Application on real-world time series forecasting datasets demonstrate improvements in prediction quality for 1%-4% using mean square error measurements in channel-independent settings. The code will be available online after 1 the review.
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
UGOD, HKUST-GZ（香港科技大学（广州））；School of Software, Yunnan University（云南大学软件学院）；UGOD, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
RefineBridge: Generative Bridge Models Improve Financial Forecasting by Foundation Models
Department of Electrical and Electronic Engineering, Imperial College London, UK（帝国理工学院伦敦分校电子与电气工程系）；Department of CST, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）
AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment
Wageningen University and Research（瓦赫宁根大学与研究中心）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；Weizmann Institute of Science（魏茨曼科学研究所）
NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series
University of California, Santa Barbara（加州大学圣巴bara分校）；Cisco Research（思科研究）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；Northwestern University（西北大学）
DPSformer: A long-tail-aware model for improving heavy rainfall prediction
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University（福建省智慧城市传感与计算重点实验室，信息学院，厦门大学）
DP-aware AdaLN-Zero: Taming Conditioning-Induced Heavy-Tailed Gradients in Differentially Private Diffusion
School of Computer Science（计算机科学学院）；Big Data, Minjiang University, Fuzhou, Fujian, China.（大数据，闽江大学，福州，福建，中国）；School of Information, Renmin University of China, Beijing, China（信息学院，中国人民大学，北京，中国）
