Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting
School of Computing and Artificial Intelligence, Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China（计算机学院与人工智能学院，可持续城市智能交通工程研究中心，教育部，集成交通运输大数据应用技术国家工程实验室，四川省制造业链条协同与信息支撑技术重点实验室，西南交通大学，成都611756，中国）；School of Computing, The University of Leeds, LS2 9JT Leeds, U.K.（计算机学院，利兹大学，利兹，英国）
Qi Xiong, Kai Tang, Minbo Ma, Ji Zhang, Jie Xu, Tianrui Li
发表信息：
IEEE Transactions on Knowledge and Data Engineering, 2025
Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by \textbf{1.47\%} to \textbf{9.19\%} and change value errors by \textbf{4.57\%} to \textbf{15.78\%}, highlighting its substantial performance improvements.
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
University of Electronic Science and Technology of China（电子科技大学）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Cambridge（剑桥大学）
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Geneva School of Business Administration, HES-SO University of Applied Sciences of Western Switzerland（日内瓦商学院，瑞士西部应用科学与艺术大学）；University of Geneva（日内瓦大学）
交通时间序列预测是具有挑战性的，因为来自不同位置的复杂时空动态时间序列通常具有不同的模式;并且对于相同的时间序列，模式可能随时间而变化，例如，在一天中存在显示出更强的时间相关性的某些时段。尽管最近的预测模型，特别是基于深度学习的模型，显示出有希望的结果，但它们受到时空不可知论的影响。这样的时空不可知模型采用共享参数空间，而不管时间序列位置和时间段，并且它们假设时间模式在位置之间是相似的，并且不会在时间上演变，这可能并不总是成立，从而导致次优结果。在这项工作中，我们提出了一个框架，旨在把时空不可知模型时空感知模型。为此，我们将来自不同位置的时间序列编码为随机变量，从中生成特定于位置和随时间变化的模型参数，以更好地捕捉时空动态。我们展示了如何整合的框架与规范的注意，使时空意识的注意。其次，为了补偿时空感知模型参数生成过程中引入的额外开销，提出了一种新的窗口注意机制，将复杂度从二次降低到线性，使得时空感知注意也具有竞争效率.我们在四个交通时间序列数据集上展示了强有力的经验证据，其中所提出的时空感知注意力在准确性和效率方面优于最先进的方法。这是“Towards Spatio-Temporal Aware Traffic Time Series Forecasting”的扩展版本，将出现在ICDE 2022 [1]中，包括其他实验结果。
Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting
Northwestern Polytechnical University（西北工业大学）；City University of Hong Kong（香港城市大学）；Shenzhen Research Institute, City University of Hong Kong（香港城市大学深圳研究院）；Northwest University（西北大学）
APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting
Hohai University（河海大学）；Key Laboratory of Water Big Data Technology of Ministry of Water Resources（水利部水大数据技术重点实验室）；College of Computer and Software, Hohai University（河海大学计算机与软件学院）
Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering
State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京先进创新中心）；Beihang University（北京航空航天大学）；HKGAI（Hong Kong University of Science and Technology）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究 institute）；Southern University of Science and Technology（南方科技大学）；Department of Data Science（数据科学系）
