As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting becomes increasingly crucial across various domains. This is highlighted by recent advancements in representation learning within the field. This study introduces a novel multi-view approach for time series forecasting that innovatively integrates trend and seasonal representations with an Independent Component Analysis (ICA)-based representation. Recognizing the limitations of existing methods in representing complex and high-dimensional time series data, this research addresses the challenge by combining TS (trend and seasonality) and ICA (independent components) perspectives. This approach offers a holistic understanding of time series data, going beyond traditional models that often miss nuanced, nonlinear relationships. The efficacy of TSI model is demonstrated through comprehensive testing on various benchmark datasets, where it shows superior performance over current state-of-the-art models, particularly in multivariate forecasting. This method not only enhances the accuracy of forecasting but also contributes significantly to the field by providing a more in-depth understanding of time series data. The research which uses ICA for a view lays the groundwork for further exploration and methodological advancements in time series forecasting, opening new avenues for research and practical applications.
PyPOTS Research（PyPOTS研究机构）；HKUST（香港科技大学）；King’s College London（伦敦国王学院）；University of Oxford（牛津大学）；Sichuan University（四川大学）；University of Washington（华盛顿大学）；GaTech（佐治亚理工学院）；HKUST(GZ)（香港科技大学（广州））；Northwest University（西北大学）；Squirrel AI（松鼠AI）
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Beijing Institute of Technology（北京理工大学）；Tongji University（同济大学）；University of Oxford（牛津大学）；University of Technology Sydney（悉尼科技大学）；University of Macau（澳门大学）；USTC（中国科学技术大学）；HeFei University of Technology（合肥工业大学）；Macquarie University（麦考瑞大学）
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting
Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究院）；Southern University of Science and Technology（南方科技大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Center for Spatial Information Science, University of Tokyo（东京大学空间信息科学中心）
HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting
Urban AI Institute, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院城市人工智能研究所）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China（电子科技大学基础与前沿科学学院）；Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education（北京大学高可信软件技术重点实验室）；School of Computer Science, Peking University（北京大学计算机学院）；China Mobile Information Technology Center（中国移动信息科技中心）；PyPOTS Research（PyPOTS研究团队）；Squirrel Ai Learning
针对时间序列预测的准确性与效率需求，提出基于证据理论的 Time Evidence Fusion Network (TEFN)，通过 BPA 模块从通道和时间维度捕捉不确定性并进行多源信息融合，在保持低复杂度和高可解释性的同时实现了领先的预测性能。
Grey-informed neural network for time-series forecasting
School of Communication, Qufu Normal University（曲阜师范大学传媒学院）；School of Computer Science and Information Engineering, Chuzhou Univeristy（滁州学院计算机科学与信息工程学院）
STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting
Zhejiang University（浙江大学）；South China University of Technology（华南理工大学）；The University of Sydney（悉尼大学）；Chongqing University（重庆大学）；Zhejiang Institute of Industry and Information Technology（浙江工业信息技术研究院）
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
AI & ML Group, TU Darmstadt（AI与机器学习小组，德累斯顿理工大学）；TU Eindhoven（埃因霍温理工大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, TU Darmstadt（认知科学中心，德累斯顿理工大学）
Harvard University, Institute for Quantitative Social Science（哈佛大学量化社会科学研究所）；Stockholm University Demography Unit（斯德哥尔摩大学人口学单位）；Malmö University, Department of Global Political Studies（马尔默大学全球政治研究系）；University of Catania, Department of Political and Social Sciences（卡塔尼亚大学政治与社会科学系）；University of Toronto, Graduate School of Public and International Affairs（多伦多大学公共与国际事务研究生院）
NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting
University of Science and Technology of China（中国科学技术大学）；Tianjin University（天津大学）；Xi’an Jiaotong University（西安交通大学）；University of Cambridge（剑桥大学）；YMSC, Tsinghua University（清华大学数学科学中心）
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Southern University of Science and Technology（南方科技大学）；The University of Tokyo（东京大学）；Hong Kong University of Science and Technology（香港科技大学）；Squirrel AI（松鼠AI）；Jilin University（吉林大学）
