Time Series Forecasting

Follow this topic Ask about this topic

Latest papers 667

139 papers in the last four weeks, up 101% on the four weeks before. 0.9% of all new papers.

All topics

Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamics
Sep 30, 2026Tao Liu, Ge He, Dongyu Liang +1Time Series Forecasting Proper Orthogonal Decomposition
Lawrence Technological University · North Carolina State University
OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning
Sep 30, 2026Tony Chen, Timo Stoffregen, Maxwell Xu +8Temporal Reasoning in Language Models Time Series Forecasting
Columbia University, USA · Stanford University, USA · Aionic Labs, Switzerland +4
Beyond Model Ranking: Regime Diagnosis for Distributional-Statistical Misspecification in Industrial Time-Series Forecasting
Sep 30, 2026Pengyu Nie, Chenglang Xu, Yaoshi Chen +4Time Series Forecasting Forecasting Benchmarks
JD.com, Inc.
Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting
Sep 30, 2026Yeryeong Kwak, Yoo-Min Jung, Jonghun ParkMultivariate Time Series Forecasting Time Series Forecasting
Seoul National University Seoul, Republic of Korea
The Nixtlaverse: An Open-Source Ecosystem for Forecasting
Sep 30, 2026Olivier Sprangers, Max Mergenthaler Canseco, Marco Peixeiro +8Time Series Forecasting Forecasting Benchmarks
Nixtla, San Francisco, CA, United States
RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting
Sep 30, 2026Hao-Nan Shi, Tong Wu, Chen-Cong Sun +3Residual Learning Time Series Forecasting
School of Artificial Intelligence, Nanjing University · National Key Laboratory for Novel Software Technology, Nanjing University · Nanjing, China
Towards Robust Time Series Learning via Capacity-Centric Modulation
Sep 30, 2026Siru Zhong, Senzhang Wang, James T. Kwok +1Time Series Classification Neural Network Robustness
Data Science & Analytics Thrust, The Hong Kong University of Science and Technology (GZ), Guangzhou, China · School of Computer Science & Engineering, Central South University, Changsha, China · Dept. of Computer Science & Engineering, The Hong Kong University of Science and Technology, Hong Kong
LogiC-Diff: Embedding Security Properties Into AI-Enabled Cyber-Physical Systems
Sep 29, 2026Ziyan An, John Stankovic, Meiyi MaCyber-Physical Systems Time Series Forecasting
Department of Computer Science Vanderbilt University Nashville, TN · Department of Computer Science University of Virginia Charlottesville, VA
Latent Inference-Time Guidance of Time Series Foundation Models
Sep 29, 2026Chloé Hashimoto-Cullen, Amaury Durand, Laurent Bozzi +3Time Series Forecasting Inference-Time Guidance
LPSM, Sorbonne Université · EDF R&D · UCL
An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
Sep 29, 2026Marco Veneriano, Ani Gjergji, Sebastiano Bellani +3Time Series Forecasting Predictive Maintenance
MaLGa Center Universit`a degli Studi di Genova Genoa, Italy · Antares Electrolysis S.r.l. Genoa, Italy
Volatility-Clustering Adaptation for Financial Time Series
Sep 29, 2026Manh Nguyen, Minh Hoang Nguyen, Huu Hiep Nguyen +2Time Series Forecasting Financial Time Series
Applied Artificial Intelligence Initiative, Deakin University, Australia
GARDiff: Graph-Aligned Residual Diffusion for Probabilistic Multivariate Time-Series Forecasting
Sep 29, 2026Rui Han, Min Yang, Xu Zhang +3Multivariate Time Series Forecasting Graph Structure Learning
Shandong University, Jinan, China · Macquarie University, Sydney, Australia · China University of Petroleum (East China), Qingdao, China +1
Loss-Guided Pretraining Data Selection for Time-Series Foundation Models
Sep 29, 2026Yike Li, Shaoxu Song, Jianmin WangTime Series Forecasting Training Data Selection
Tsinghua University
JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
Sep 29, 2026Donguk Kwon, Wooseok Jeong, Dongha LeeTime Series Forecasting LLM-Based Time Series Forecasting
Yonsei University · Konkuk University
State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting
Sep 29, 2026Xu Yuqing, Zhou Liguo, Sun Ze +2Time Series Forecasting Solar Power Forecasting
Huaibei Normal University, China
GNA: Granular Neighbor Assembly for Retrieval-Augmented Multivariate Time-Series Forecasting
Sep 28, 2026Vincent UhseMultivariate Time Series Forecasting Time Series Forecasting
CERN
Introducing the CZAR Loss: A Tailored Objective Function for Financial Log-Return Predictions
Sep 28, 2026Joel Pfeffer, J. M. Diederik Kruijssen, Florian Stecker +1Financial Forecasting Time Series Forecasting
Allora Foundation · LJMU · Liverpool John Moores University
SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting
Sep 28, 2026Bang Hu, Changze Lv, Mingjie Li +3Multivariate Time Series Forecasting Efficient Neural Network Inference
School of Computer Science, Fudan University, Shanghai, China
XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching
Sep 28, 2026Ziyang Zhang, Hanyin Cheng, Xiangfei Qiu +3Multivariate Time Series Forecasting Time Series Forecasting
East China Normal University
Context-dependent time-series prediction via HyperReservoirs
Sep 28, 2026Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki +1Time Series Forecasting Reservoir Computing
Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Sep 28, 2026Zehao Xiao, Shifeng Xie, Lei Zan +5Time Series Forecasting Prompt Tuning
Huawei Noah’s Ark Lab, Paris, France · LIPADE, Universit´e Paris Cit´e, Paris, France · Huawei Noah’s Ark Lab, Shenzhen, China
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
Sep 28, 2026Jia Long Bao, Ali Mohammed Mansoor Alsahag, Seyed Sahand Mohammadi ZiabariExplainable Time Series Forecasting Time Series Forecasting
Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting
Sep 28, 2026Yuanyuan Deng, Mykola Pechenizkiy, Songgaojun DengMultivariate Time Series Forecasting Feature Interaction Modeling
Eindhoven University of Technology Eindhoven, The Netherlands
LTV-CTDNet: Compositional Turning Decomposition for Short-Term Turning-Movement Forecasting
Sep 27, 2026Md Atiqur Rahman Mallick, Kamrul Hasan, Robert T. WhiteTime Series Forecasting Traffic Flow Estimation
Graduate Student, Department of Electrical and Computer Engineering Tennessee State University, Nashville, TN, 37209 · Assistant Professor, Department of Electrical and Computer Engineering Tennessee State University, Nashville, TN, 37209 · TSMO Manager Nashville Department of Transportation & Multimodal Infrastructure Nashville, TN 37210
RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting
Sep 27, 2026Tong Liu, Lanmiao Liu, Xiang HuFinancial Forecasting Time Series Forecasting
Zircon Security · Utrecht University · The Max Planck Institute for Psycholinguistics +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
ViBR-WM: Visual Bayesian Regression for World Modeling
Sep 27, 2026Jifan Li, Ning NingWorld Model Learning Time Series Forecasting
Department of Statistics, Texas A&M University College Station, TX 77843
Correct then Forecast: Observer State-Space Models for Time Series Forecasting
Sep 27, 2026Alexis-Raja Brachet, Guillaume Clavier--Frémond, Abdelhakim Ziani +2Latent Dynamics Modeling Time Series Forecasting
MICS, CentraleSup´elec, Universit´e Paris-Saclay, France · CentraleSup´elec, IETR UMR CNRS 6164, France · Universit`a di Torino, Torino, Italy.
RAEGL: Risk-Aware Evidence-Gated Learning for Selective Contextual Routing under Temporal Shift
Sep 27, 2026Yifan GuoDistribution Shift Selective Prediction
Stony Brook University

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamics

Tao Liu, Ge He, Dongyu Liang, Wujie Wen

Lawrence Technological University · North Carolina State University

Time Series Forecasting Proper Orthogonal Decomposition Hybrid Quantum-Classical ML Reduced-Order Modeling Variational Quantum Circuits

Open paper AskPDF arXiv

We evaluate hybrid quantum-classical machine learning for the reduced-order prediction of spatiotemporal brain deformation fields. To mitigate the computational intractability of high-dimensional displacement fields, we employ Proper Orthogonal Decomposition (POD) to project the data into a compact latent space. Within this framework, we formulate two distinct learning objectives: static temporal-to-latent regression and autoregressive latent state forecasting. We systematically benchmark compact classical baselines against both minimal and enhanced hybrid quantum architectures. Our results demonstrate that classical networks provide the strongest baselines in the present setting. For static regression, a classical POD-MLP outperforms all evaluated quantum variants, although an enhanced Variational Quantum Circuit (VQC) substantially improves upon a minimal VQC baseline. For temporal forecasting, a classical POD-LSTM delivers superior predictive accuracy and statistical robustness compared to an enhanced Quantum LSTM (QLSTM) across varying history windows and random initializations. Overall, this study establishes reduced-order physical field learning as a rigorous testbed for near-term QML, highlighting that while hybrid enhancements successfully recover expressivity in weak quantum circuits, classical architectures retain a definitive advantage in both fidelity and stability.