Long-Term Time Series Forecasting

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Long-term time series forecasting uses past observations to predict many steps into the future, supporting planning when decisions must be made well ahead of time. Methods range from trend and seasonal decomposition, linear models, and patch-based mixers to Transformers, state-space models, and graph networks for linked locations or variables. Some approaches add physical dynamics, external signals, or retrieval of similar historical cases, while probabilistic models represent uncertainty as well as a single forecast. A central challenge is keeping forecasts stable under distribution shifts and accumulated errors; strong pointwise accuracy can still come at the cost of realistic uncertainty, structural fidelity, or efficiency.

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115 papers20 papers in the last four weeks, up 82% on the four weeks before. 0.1% of all new papers.

RankFormer: Rethinking Time Series Transformers through Representation Capacity Analysis and Progressive Decoding
Oct 27, 2026Peiwang Tang, Cheng HE, Xijie Liang +1Multivariate Time Series Forecasting Time Series Forecasting
University of Science and Technology of China, Hefei, Anhui, China · State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China · Shanghai Black Wing Asset Management, Shanghai, China
GraphCast-Mamba: Learning Non-Markovian Corrections for Weather Forecasting
Oct 20, 2026Ilya Vilkoviskiy, Liang-Hong Mo, Suozhi Huang +1Weather Forecasting Time Series Forecasting
Department of Physics, Princeton University, Princeton, NJ 08544, USA · Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA · École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland +2
Compose the Horizon: Parameter-Shared Blockwise Forecasting under a Sequential-Compute Trade-off
Oct 17, 2026Zeyan Li, Weiheng Li, Shengda Zhuo +3Long-Term Time Series Forecasting Time Series Forecasting
Shanghai Jiao Tong University Shanghai, China · Jinan University Guangzhou, China · The University of Chicago Chicago, IL, USA +1
LagNet: Lag-Structured Operators for Long-Term Time Series Forecasting
Oct 16, 2026Zhenan Yu, Guangxin Jiang, Jiuyun Jiang +1Long-Term Time Series Forecasting Time Series Forecasting
Harbin Institute of Technology
Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs
Oct 15, 2026Jinyi Duan, Qian Li, Biao TangEpidemic Forecasting Spatial Epidemic Modeling
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China . · School of Mathematics and Statistics, Xi’an University of Technology, Xi’an, 710054, People’s Republic of China . · Center for Intersection of Mathematics and Life Sciences, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China .
ReAl: Refine neural operators via aligning dynamical flow
Oct 14, 2026Jing Xu, Zenglin Xu, Li Yi +2PDE Surrogate Modeling PDE Operator Learning
Shanghai Academy of AI for Science · Fudan University
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
Oct 14, 2026Chengyi Pan, Huangqimei Zheng, Hao XueTime Series Modeling Exogenous Time Series Forecasting
UGOD, HKUST-GZ · School of Software, Yunnan University · UGOD, The Hong Kong University of Science and Technology (Guangzhou)
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting
Oct 14, 2026Xinjie Lan, Yunbo Mei, Leheng Zhou +1Long-Term Time Series Forecasting Non-Stationary Time Series Forecasting
Rice University · University of Hong Kong · University of North Carolina at Charlotte
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting
Oct 13, 2026Yangze Li, Zhengyang Zhou, Qihe Huang +3Long-Term Time Series Forecasting Multivariate Time Series Forecasting
University of Science and Technology of China (USTC), Hefei, China · Suzhou Institute for Advanced Research, USTC, Suzhou, China
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version
Oct 12, 2026Kien Le Trung, Tung Kieu, Dinh Duc Nha Nguyen +3Air Quality Prediction Long-Term Time Series Forecasting
VinUniversity Vietnam · Aalborg University Denmark · CECS, VinUniversity Vietnam +2
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics
Oct 11, 2026Sarang Rajendra Patil, Vishal Fenn, Mengjia XuLong-Term Time Series Forecasting Time Series Forecasting
Department of Data Science New Jersey Institute of Technology Newark, NJ 07102, USA
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection
Oct 10, 2026Josef Koumar, Karel Hynek, Kamil Jerabek +1Forecasting Benchmarks Long-Term Time Series Forecasting
Czech Technical University in Prague, Thákurova 9, 160 00 Prague 6, Czech Republic · CESNET a.l.e., Generála Píky 430/26, 160 00 Prague 6, Czech Republic
HART: Hybrid Attention-RWKV Time series Foundation Model
Oct 9, 2026Qingwen Lin, Boyan Xu, Shifeng Xie +3Time Series Forecasting Time-Series Foundation Models
School of Computer Science, Guangdong University of Technology · Huawei Noah’s Ark Lab · LIPADE, Université Paris Cité, Paris, France +2
AliO: Output Alignment Matters in Long-Term Time Series Forecasing
Oct 8, 2026Kwangryeol Park, Jaeho Kim, Seulki LeeFrequency-Domain Time Series Modeling Long-Term Time Series Forecasting
Artificial Intelligence Graduate School Ulsan National Institute of Science & Technology (UNIST), South Korea · Artificial Intelligence Korea University, South Korea · Department of Computer Science and Engineering Ulsan National Institute of Science & Technology (UNIST), South Korea
SACQ: Structured Decoding with Memory-Conditioned Refinement for Long-Horizon Forecasting
Oct 8, 2026Guo Cheng, Zhengzhuo Xu, Chenchen Jing +1Robust Time Series Forecasting Long-Term Time Series Forecasting
University of Science and Technology Beijing, China · Zhejiang University of Technology, Hangzhou, China · Institution of Artificial Intelligence, University of Science and Technology Beijing, China
CARE: A Lightweight Plug-in Gated Correction and Uncertainty-aware Module for Long-term Time Series Forecasting
Oct 8, 2026Guo Cheng, Changlong Lv, Jingyi HouMultivariate Time Series Forecasting Long-Term Time Series Forecasting
School of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China · Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China · Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China
RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting
Oct 8, 2026Shengsheng Lin, Jing Hu, Zhengyang Hu +8Exogenous Time Series Forecasting Time Series Forecasting
South China University of Technology, China · Didi Chuxing, China · The University of Hong Kong, China
Structure-Aware Graph Abstention for Reliable Selective Forecasting
Oct 6, 2026Jianxiang Xie, Belal AlsinglawiMultivariate Time Series Forecasting Long-Term Time Series Forecasting
University of New South Wales · Zayed University
Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting
Oct 2, 2026Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak +2Time Series Forecasting Long-Term Time Series Forecasting
VWFS Data Analytics Research Center (VWFS-DARC), Hildesheim, Germany
Dual-Context Analog Retrieval for Time Series Forecasting
Oct 2, 2026Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak +2Long-Term Time Series Forecasting Time Series Forecasting
ISMLL, University of Hildesheim · VWFS Data Analytics Research Center (VWFS-DARC), Hildesheim, Germany
Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting
Sep 29, 2026Anthony Frion, Lucas Drumetz, Guillaume Tochon +3Probabilistic Time Series Forecasting Long-Term Time Series Forecasting
Institute of coastal modelling Helmholtz-Zentrum Hereon · IMT Atlantique Lab-STICC, UMR CNRS 6285, Brest, France · LRE EPITA, Le Kremlin-Bicêtre, France +1
From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting
Sep 27, 2026Chaoqi Zhang, Yu Wang, Haixu TangLong-Term Time Series Forecasting Time Series Forecasting
Indiana University Bloomington, IN, USA
Diffusion-Based Rollouts as a Stabilization Mechanism for Long-Horizon Environmental Forecasting
Sep 27, 2026Marina Vicens-Miquel, Amy McGovern, Aaron J. Hill +2Precipitation Forecasting Long-Term Time Series Forecasting
University of Oklahoma, School of Meteorology, Norman, Oklahoma · University of Oklahoma, School of Computer Science, Norman, Oklahoma · NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, Norman, Oklahoma +2
Aurora-X: Built for Extreme Time Series Forecasting
Sep 25, 2026Xingjian Wu, Chenjuan Guo, Xiangfei Qiu +6Time Series Forecasting Time-Series Foundation Models
East China Normal University
Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction
Sep 23, 2026Ragamayi Puli, Shunya NagashimaMultivariate Time Series Forecasting Long-Term Time Series Forecasting
Neurogica Inc.
Recovering Lost Details: Multi-Scale Frequency Compensation for Long-Term Time Series Forecasting
Sep 21, 2026Runmin Zou, Siyi Xie, Yaohui Huang +1Long-Term Time Series Forecasting Time Series Forecasting
Central South University Changsha, Hunan, China
One Patch, Three Roles: What Is Actually Coupled in Autoregressive Time-Series Forecasting?
Sep 20, 2026Ziang Li, Yue Huang, Guoxu Zhou +4Long-Term Time Series Forecasting Time Series Forecasting
Guangdong University of Technology · Guangdong Polytechnic Normal University · Harbin Institute of Technology
Monotone-Constrained Diffusion Models for Long-Horizon Production Forecasting
Sep 18, 2026Temesgen Mikael Abraha, Yves LucetTime Series Forecasting Long-Term Time Series Forecasting
Department of Computer Science, I. K. Barber Faculty of Science University of British Columbia Kelowna, BC V1V 1V7, Canada
SETTer: Sparse-Encoder Transformer for Long-term Multivariate Time Series Forecasting
Sep 17, 2026Abraham Ezema, Chijioke Eze, Ferdinanda Ponci +1Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Institute for Automation of Complex Power Systems, RWTH Aachen University, 52064 Aachen, Germany · RWTH Aachen University, 52064 Aachen, Germany · institute for Monitoring and Distributed Control for Power Systems Teaching and Research Area, RWTH Aachen University, 52064 Aachen, Germany +1
OceanMoE: Structured Conditional Sparse Computation for Long-Horizon Multivariate Ocean Forecasting
Sep 17, 2026Yishun Zhu, Jian WangMultivariate Time Series Forecasting Long-Term Time Series Forecasting
Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences Zhejiang, China · Computer Network Information Center, Chinese Academy of Sciences Beijing, China

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RankFormer: Rethinking Time Series Transformers through Representation Capacity Analysis and Progressive Decoding

Peiwang Tang, Cheng HE, Xijie Liang, Defu Lian

University of Science and Technology of China, Hefei, Anhui, China · State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China · Shanghai Black Wing Asset Management, Shanghai, China

Multivariate Time Series Forecasting Time Series Forecasting Time Series Transformers Long-Term Time Series Forecasting

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Multivariate time series forecasting plays a critical role in Web and cyber-physical systems, where large-scale services and IoT infrastructures generate complex temporal data. Transformer-based models have achieved strong empirical performance in this domain, yet the principles governing the expressive capacity of their learned representations remain insufficiently understood. Moreover, many existing models rely on simplified decoding strategies, including channel-independent modeling and direct projection heads, which limit their ability to capture complex multivariate dynamics. To address these challenges, we propose RankFormer, a novel encoder-decoder architecture for multivariate time series forecasting. We establish a rank-based analysis framework to investigate how tokenization, embedding, and attention designs affect the representation capacity of time series Transformers. Our analysis reveals that patchification increases the potential representation capacity by aggregating multiple temporal observations into each token, while subsequent encoding operations determine how much of this capacity is realized. Guided by these insights, we develop a rank-enhancing encoder with Hourglass Attention to improve rank stability during information interaction. Furthermore, we introduce the Interactive Progressive Decoder (IPD), which improves future trajectory modeling through global-guided cross-channel interaction and progressive refinement. Extensive experiments on real-world long-term forecasting benchmarks demonstrate that RankFormer achieves competitive performance against state-of-the-art methods. Code repository: https://github.com/TangPeiwang/Rankformer.