Time Series Forecasting

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

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Exposure Bias as Epistemic Underidentification in Recursive Forecasting
Jun 11, 2026Riku Green, Zahraa S. Abdallah, Telmo M Silva FilhoDistribution Shift Time Series Forecasting
University of Bristol, UK
Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systems
Jun 11, 2026Mansi Od, Param Pathak, Nouhaila Innan +1Time Series Forecasting Reservoir Computing
University of Greenwich, London, United Kingdom · Fractal Analytics, QuantumAI Lab, Mumbai, Maharashtra, India · eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE +1
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Jun 10, 2026Yifan Gao, Yanmin Gong, Yun Shi +1Type 1 Diabetes Time Series Forecasting
Department of Information Systems and Cybersecurity The University of Texas at San Antonio San Antonio, USA · School of Engineering Medicine Texas A&M University Houston, USA · Department of Family and Community Medicine The University of Texas at San Antonio San Antonio, USA
Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI
Jun 10, 2026Ali M Karaoglu, Shreyank N GowdaExplainable Artificial Intelligence Zero-Shot Text Classification
School of Computer Science, University of Nottingham, Nottingham, United Kingdom
When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
Jun 10, 2026Rupasree Dey, Abdul Matin, Nathan Orwick +3Multivariate Time Series Forecasting Time Series Forecasting
Department of Computer Science Colorado State University Fort Collins, Colorado, USA · Department of Soil and Crop Sciences Colorado State University Fort Collins, Colorado, USA
TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models
Jun 10, 2026Kanghui Ning, Yushan Jiang, Kashif Rasul +3Adaptive Model Routing Time Series Forecasting
School of Computing, University of Connecticut, Storrs, USA · Department of Machine Learning Research, Morgan Stanley, New York, USA.
CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting
Jun 9, 2026Yosuke Yamaguchi, Issei Suemitsu, Yuki Kajihara +1Synthetic Data Pretraining Zero-Shot Time Series Forecasting
Research & Development Group, Hitachi Ltd., Tokyo, Japan
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
Jun 9, 2026Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers +4Multivariate Time Series Forecasting Residual Learning
Artificial Intelligence Department, RobotBulls Labs Geneva, Switzerland · Department of Electrical and Computer Engineering, North South University Dhaka, Bangladesh
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
Jun 9, 2026Xingyu Zhang, Jingyao Wang, Xin Yu +4Time Series Forecasting Probabilistic Time Series Forecasting
University of Chinese Academy of Sciences · Institute of Software, Chinese Academy of Sciences
Structured Adaptive Tensor Prediction for Streaming Data
Jun 8, 2026Zhen Qin, Yang ChenTime Series Forecasting Online Learning
Michigan Institute for Computational Discovery and Engineering, Department of Statistics, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA · Department of Statistics, University of Michigan, Ann Arbor, MI 48109 USA
Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum
Jun 8, 2026Abd Elghani Meliani, Arora Sagar, Adlen Ksentini +1Time Series Forecasting Automated Machine Learning
Eurecom Campus SophiaTech, Biot, France · OpenAirInterface Campus SophiaTech, Biot, France
Report the Floor: A Training-Free Conformal Interval Is a Mandatory Baseline for Probabilistic Time-Series Forecasting
Jun 8, 2026Valery ManokhinTime Series Forecasting Forecasting Benchmarks
Independent researcher.
Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall
Jun 8, 2026Gijs van Nieuwkoop, Siamak MehrkanoonPrecipitation Forecasting U-Net
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Does Normalization Choice Matter for Causal Large Time-Series Models?
Jun 8, 2026Samy-Melwan Vilhes, Gilles Gasso, Mokhtar Z AlayaTime Series Forecasting Non-Stationary Time Series Forecasting
LMAC · INSA Rouen Normandie, Univ Rouen Normandie Univ, LITIS UR4108, F-76000 Rouen, France · Univ de Technologie de Compi`egne, LMAC EA 222, F-60203 Compi`egne, France
FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting
Jun 8, 2026Qianyang Li, Xingjun Zhang, Shaoxun Wang +2Sparse Mixture-of-Experts Time Series Forecasting
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China · Department of Research and Development, Shandong New Beiyang Information Technology Co., Ltd., WeiHai 264200, China · Department of Computer Science and Technology, Tsinghua University, Beijing 100084
Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
Jun 7, 2026Jiahui Huang, Ao Luo, Lei Liu +6Zero-Shot Time Series Forecasting Time Series Forecasting
School of Information Science and Technology, University of Science and Technology of China, Hefei, 230022, China · China Electric Power Research Institute, Beijing, 100192, China
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
Jun 7, 2026Peiliang Gong, Emadeldeen Eldele, Chenyu Liu +8Zero-Shot Time Series Forecasting Time Series Forecasting
College of Computing and Data Science, Nanyang Technological University, Singapore · Department of Computer Science, Khalifa University, UAE · Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China +1
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
Jun 6, 2026Gerçek Budak, Faraz Gholamzadeh Gharehgheshlaghi, Melika Barjesteh Vaezi +1Time Series Forecasting Combinatorial Optimization
Department of Industrial Engineering, Ankara Yıldırım Beyazıt University, Keçiören, Ankara 06010, Türkiye · Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX, United States · Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
Jun 6, 2026Aaryan Nagpal, Debdeep Sanyal, Murari Mandal +2Synthetic Data Pretraining Time Series Forecasting
Birla AI Labs, Mumbai, India · KIIT, Bhubaneswar, India · BITS Pilani, Pilani, India
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
Jun 5, 2026Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi +2Zero-Shot Time Series Forecasting Time Series Forecasting
Sistemi 2000 srl, Civitanova Marche, Italy · Department of Engineering ”Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy · Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, Italy
GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
Jun 5, 2026Nick Teutschmann, Laura Crocetti, Fanny Lehmann +2Self-Supervised Time Series Representation Learning Time Series Forecasting
Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland · Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland · ETH AI Center, Switzerland +1
Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions
Jun 5, 2026Dang Viet Anh Nguyen, Alma Fazlagic, Kristine Pryds Loft +1Time Series Forecasting Spatiotemporal Forecasting
Department of Technology, Management, and Economics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark
DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
Jun 5, 2026Yongchao Li, Yang Li, Zhuoxuan Li +4Intelligent Transportation Systems Time Series Forecasting
School of Transportation, Southeast University, Nanjing 211189, China · Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 211189, China · Department of Data Science, City University of Hong Kong, Hong Kong 999077, China +6
GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting
Jun 5, 2026Baiying Lu, Zhaohui Liang, Ryan Pontius +2Zero-Shot Time Series Forecasting Time Series Forecasting
Department of Computer Science Dartmouth College Hanover, NH 03784 · Department of Computer Science Emory University Atlanta, GA 30322 · Quantitative Biomedical Sciences Dartmouth College Hanover, NH 03784
Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting
Jun 4, 2026Jingxin Zhang Xiaoqin WangTime Series Forecasting Multi-Scale Feature Fusion
School of Automation, Southeast University, Nanjing, 210096, China
Adaptive Oscillatory-State Alignment for Time Series Forecasting
Jun 4, 2026Zhangyao Song, Chaofeng Qu, Chao Zha +3Long-Term Time Series Forecasting Time Series Forecasting
School of Cyber Science and Engineering, Southeast University, Nanjing, China · State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China · School of Information Science and Engineering, Southeast University, Nanjing, China
TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
Jun 4, 2026Etienne Le Naour, Tahar Nabil, Adrien PetraliaIrregular Time-Series Modeling Time Series Imputation
1EDF R&D, Palaiseau, France
GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis
Jun 4, 2026Oleeviya Babu Poikarayil, Cédric Schockaert, Abdulrahman Nahhas +3Efficient Neural Network Inference Large Language Model-Guided NAS
Paul Wurth S.A., Luxembourg · Otto-von-Guericke University, Magdeburg, Germany · Technical University of Munich, Germany
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Jun 3, 2026Yiming Lu, Sihang Zeng, Zhengxu Tang +3Epidemic Forecasting Time Series Forecasting
Emory University · University of Washington
Evidence-Guided Neural Architecture Selection under Uncertainty for Subject-Specific Blood Glucose Forecasting
Jun 3, 2026Md Azharul Islam, Dwyer Deighan, Tarunraj Singha +1Time Series Forecasting Blood Glucose Forecasting
Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA · Computational and Data-Enabled Sciences, University at Buffalo, Buffalo, NY, USA · University at Buffalo, Buffalo, NY, USA

Jun 11, 2026J/K move · Enter open · S save

Exposure Bias as Epistemic Underidentification in Recursive Forecasting

Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho

University of Bristol, UK

Distribution Shift Time Series Forecasting Epistemic Uncertainty

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Recursive multi-step forecasting is usually framed as distribution shift: models are trained on observed histories but deployed on their own predictions. We show this framing is incomplete by proving that, under partial observability or state truncation, recursive rollout is also an epistemic underidentification problem. Even with deterministic latent dynamics, one-step Bayes supervision identifies behavior only on observed contexts and need not identify the deployed recursive predictor once rollout queries self-generated induced states whose correct local targets are not determined by numeric state alone. We formalize this with induced states

Z

and provenance variables

P

, and derive a decomposition of induced-state error into teacher-forcing/rollout mismatch, representation--class approximation, and provenance information gaps. Empirically, we show that rollout enters a distinct induced-state regime, that fixed induced states define a distinct local corrective task, and that closed-loop gains arise not only from local adaptation but also from changing the induced states visited during rollout. Using a simple binary provenance encoding, provenance-aware correction can further improve performance, though gains are conditional rather than uniform. These results recast exposure bias as reasoning under self-induced epistemic uncertainty.