Time Series Forecasting

Latest papers 667

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

WPBench: A Comprehensive Benchmark for Wind Power Forecasting
Sep 21, 2026Yuhan Zhu, Jilin Hu, Xinying Cai +7Multivariate Time Series Forecasting Benchmark Design
School of Data Science & Engineering, East China Normal University, Shanghai, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, China
MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting
Sep 21, 2026Xinying Cai, Junkai Lu, Yuhan Zhu +3Multivariate Time Series Forecasting Time Series Forecasting
East China Normal University
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
The Operational Value of Spatial Dependence in Renewable Forecast Scenarios for Single-Period Economic Dispatch: A Controlled Ablation Study
Sep 21, 2026Jayakumar ManoharanTime Series Forecasting Probabilistic Forecasting
Electric Power Research Institute (EPRI), Charlotte, NC, USA
TEMPER: Temporal Encoder-Masked Probabilistic Ensemble Regressor for Time-Series Forecasting
Sep 20, 2026Giancarlo VercellinoTime Series Forecasting Probabilistic Time Series Forecasting
Independent researcher
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
HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting
Sep 20, 2026Nabeel Ahmad SaiddTime Series Forecasting Financial Time Series
Dr. APJ Abdul Kalam Technical University
Leaky-integrator reconstruction: taming error accumulation in recursive differenced time-series forecasting
Sep 20, 2026Zijiang YangTime Series Forecasting Non-Stationary Time Series Forecasting
New York University, Tandon School of Engineering, Brooklyn, NY 11201
CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning
Sep 20, 2026Minkyoung Kim, Daeun Ji, Yohan Lee +2Time Series Forecasting LLM-Based Time Series Forecasting
Graduate School of Information, Yonsei University, Seoul, Republic of Korea
Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets
Sep 19, 2026Arkadiusz Lipiecki, Nikolaos Kourentzes, Rafal WeronElectricity Price Forecasting Time Series Forecasting
Department of Computational Social Science, Wrocław University of Science and Technology, Poland · Indicio Technologies AB, Stockholm, Sweden · Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Poland
Event Signature Transfer: Model-Agnostic Forecast Scenario Construction from Historical Events
Sep 19, 2026Karthik Sridhar, Aaditya Jain, Murari Mandal +1Time Series Forecasting Probabilistic Time Series Forecasting
Birla AI Labs · KIIT Bhubaneswar
Comparative review of hybrid forecasting models for short-term prediction of building thermal load
Sep 19, 2026Nikolaos A. Efkarpidis, Despoina Kothona, Georgios C. ChristoforidisTime Series Forecasting Building Energy Management
VIVAVIS Schweiz AG - 5405 Baden, Switzerland · Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
A Hybrid Attention Model Learning Unified Time-aware Patch Representation for Irregular Multivariate Time Series Forecasting
Sep 19, 2026Li Lin, Zhihao Lin, Qi Zhang +3Irregular Time-Series Modeling Multivariate Time Series Forecasting
Southeast University, China · Nanyang Technological University, Singapore · Timecho Ltd., China
Beyond Average Error through Oracle-Informed Stress Tests for Time-Series Forecasting
Sep 19, 2026Xu Lin, Runheng Zuo, Shengxuan Xu +3Multivariate Time Series Forecasting Time Series Forecasting
Tsinghua University
Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale
Sep 19, 2026Hong Zhang, John K. Hutchison, Rao Kotamarthi +7Time Series Forecasting Spatiotemporal Forecasting
Argonne National Laboratory, Lemont, IL 60439, USA
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
DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting
Sep 18, 2026Wentao Zhao, Hongqiang Wu, Shanghang Liu +3Financial Forecasting Time Series Forecasting
Department of Automation, Tsinghua University, Beijing, 100084, China · School of Software, Tsinghua University, Beijing, 100084, China · School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China +1
Conformal Adversarial Generative Ensemble
Sep 18, 2026Ahmad Shahi, Mamehgol Yousefi, Brendon J. Woodford +2Time Series Forecasting Ensemble Learning
Unitec Institute of Technology, Auckland, New Zealand · School of Product Design, University of Canterbury, Christchurch, New Zealand · School of Computing, University of Otago, Dunedin, New Zealand +2
A Lightweight Plug-in Gate for Transformer-Based Time-Series Forecasters
Sep 17, 2026Hongkai Zhuang, Tao Huang, Chen HouTime Series Forecasting Time Series Transformers
School of Computer and Big Data, Minjiang University, No. 200 Xiyuangong Road, Fuzhou, 350108, Fujian, China.
Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting
Sep 17, 2026Mu-En Lee, Yen-Ku Liu, Samuel Yen-Chi Chen +1Time Series Forecasting Quantum Machine Learning
University of Toronto, Toronto, ON, Canada · National Yang Ming Chiao Tung University, Hsinchu, Taiwan · Brookhaven National Laboratory, Upton, NY, USA +1
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
Physical knowledge on historical data matters more than enforcing physical constraints on the forecast
Sep 17, 2026Etienne Lehembre, Pascal Audigane, Vincent Nguyen +2Time Series Forecasting Physics-Informed ML
CA, LIFO · BRGM · LIFO +1
Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems
Sep 17, 2026Mohammed El Hanjri, Anas Abouaomar, Hamidou Tembine +1Opinion Dynamics Federated Learning Aggregation
ENSIAS, Mohammed V University in Rabat, Morocco. · Department of Electrical and Computer Engineering, School of Engineering, University of Quebec at Trois-Rivieres, Quebec, Canada.
TW3Cast: A Frozen Router of Lightly Fine-Tuned Foundation Models for Time-Series Forecasting on GIFT-Eval, Selected Entirely on the Training Split
Sep 16, 2026Nathan Thierry, Andre-Louis RochetTime Series Forecasting Time-Series Foundation Models
TW3 Partners
Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels
Sep 16, 2026Souhardya Chattopadhyay, Julian Oelhaf, Antonia Schoening +5Time Series Forecasting Electricity Load Forecasting
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg · Siemens AG, Smart Infrastructure · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden
Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?
Sep 16, 2026Yutong Feng, Bowen Liao, See Kiong Ng +1Joint-Embedding Predictive Architecture Self-Supervised Time Series Representation Learning
National University of Singapore · South China University of Technology · Hong Kong University of Science and Technology (Guangzhou)
TERN: A Delta-rule Memory with a Seasonal Reference and Online Adaptation for Epidemic Forecasting
Sep 16, 2026Shunya Nagashima, Yuta FunayamaEpidemic Forecasting Time Series Forecasting
LTS, Inc., Japan
Principled Koopman Representations with Kalman Inference for Efficient Time-Series Prediction
Sep 15, 2026Ruiquan Li, Yuheng BuKoopman Operator Learning Time Series Forecasting
Department of Computer Science, UC Santa Barbara
NanoForecast v0.5: Competitive Time Series Forecasting Through Training Pipeline Optimization
Sep 15, 2026Gautam KishoreTime Series Forecasting Forecasting Benchmarks
Eulogik
Distributed JEPA: A Self-Supervised Framework for Energy Forecasting
Sep 15, 2026Liana Toderean, Tudor Cioara, Vasilis Michalakopoulos +3Self-Supervised Time Series Representation Learning Joint-Embedding Predictive Architecture
Distributed Systems Research Laboratory, Computer Science Department, Technical University of Cluj-Napoca, G. Barițiu 26-28, 400027 Cluj-Napoca, Romania · Decision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, Ir. Politechniou 9, 157 73 Athens, Greece

Sep 21, 2026, cs.LGJ/K move · Enter open · S save

WPBench: A Comprehensive Benchmark for Wind Power Forecasting

Yuhan Zhu, Jilin Hu, Xinying Cai, Yingshan Li+6

School of Data Science & Engineering, East China Normal University, Shanghai, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, China

Multivariate Time Series Forecasting Benchmark Design Time Series Forecasting Forecasting Benchmarks Spatiotemporal Forecasting Wind Power Forecasting

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Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market operations. Progress in this field hinges on the ability to empirically and comprehensively benchmark forecasting methods. Yet existing benchmarks fall short of supporting systematic evaluation in four key aspects: 1) limited coverage of wind power scenarios across turbine scale, variable composition, and spatial structure; 2) incomplete coverage of forecasting model families; 3) evaluation metrics misaligned with wind power requirements; and 4) limited structure-aware diagnostics beyond individual temporal patterns. To address these limitations, we propose WPBench, a comprehensive, fair, and extensible benchmark for wind power forecasting. WPBench integrates 26 public datasets organized by turbine scale and variable composition, spanning single-turbine, multi-turbine, univariate, and multivariate settings. Under unified processing, training, and evaluation protocols, it benchmarks 19 representative models covering traditional methods, deep temporal models, spatio-temporal models, and foundation models. Beyond point-wise errors, WPBench assesses forecast-curve fidelity and computational efficiency, and delivers structure-aware diagnostics across temporal, variable-dependency, and spatial-dependency perspectives. Together, these capabilities enable systematic model comparison across diverse wind scenarios and provide a reusable platform for future research.