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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RIDE: An Open Dataset and Benchmark for Train Delay Prediction
Jun 3, 2026Clément Elliker, Mathis Le Bail, Clément Mantoux +2Intelligent Transportation Systems Time Series Forecasting
LIX, École Polytechnique, IP Paris, France · e.SNCF Solutions, France
Signed Dual Attention: Capturing Signed Dependencies in Time Series Forecasting
Jun 3, 2026Balthazar Courvoisier, Tristan CazenaveTime Series Forecasting Transformer Attention
Queensfield AI Technologies, Paris, France · LAMSADE, Universit´e Paris Dauphine - PSL, Paris, France
REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning
Jun 3, 2026Weijia Li, Shun Hu, Yanfei KangMultivariate Time Series Forecasting Time Series Forecasting
School of Mathematical Sciences, Beihang University, Beijing, China · School of Economics and Management, Beihang University, Beijing, China
Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
Jun 3, 2026Riku Green, Zahraa S. Abdallah, Telmo M Silva FilhoLong-Term Time Series Forecasting Time Series Forecasting
The University of Bristol Bristol, UK
Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge
Jun 2, 2026Cen LuTime Series Forecasting Forecasting Benchmarks
EPFL & Idiap Research Institute · Switzerland
Stationarity-Aware Retrieval-Augmented Time Series Forecasting
Jun 2, 2026Shiqiao Zhou, Holger Schöner, Zipeng Wu +3Time Series Forecasting Non-Stationary Time Series Forecasting
University of Birmingham Birmingham, United Kingdom · Siemens AG Munich, Germany
Adaptive Patching Is Harder Than It Looks For Time-Series Forecasting
Jun 2, 2026Federico Zucchi, Yi Xie, Chao Zhang +3Long-Term Time Series Forecasting Time Series Forecasting
ICube, University of Strasbourg, Illkirch-Graffenstaden, France · Cephalgo, Strasbourg, France · Technical University of Munich +3
Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift
Jun 2, 2026Doyeong Lim, Seungyoon Lee, In Cheol BangNeural Surrogate Modeling Continual Test-Time Adaptation
Department of Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance
Jun 2, 2026Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni +3Financial Forecasting Time Series Forecasting
National University of Singapore Singapore · National University of Singapore Asian Institute of Digital Finance Singapore · Nanyang Technological University Singapore
From Long News to Accurate Forecast: Importance-Aware Fusion and PRM-Guided Reflection for Time Series Forecasting
Jun 2, 2026Mingyang Liu, Qingcan Kang, Yuke Wang +6Process Reward Models Financial Forecasting
Department of Computer Science, City University of Hong Kong · Work done as an intern in Huawei Noah’s Ark Lab. · Huawei Noah’s Ark Lab
Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
Jun 1, 2026Waleed Esmail, Stuart Russell, Jana Klinge +2Multivariate Time Series Forecasting Time Series Forecasting
Institut für Kernphysik, Universität Münster, Wilhelm-Klemm-Straße 9, 48149, Münster · Institut für Geophysik, Universität Münster, Corrensstraße 24, 48149, Münster · James Cook University, 1 James Cook Drive, Douglas, Queensland, 4814, Australia +1
RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting
Jun 1, 2026Jainam Dhruva, Yousaf Raza, A. B. Siddique +1Multivariate Time Series Forecasting Synthetic-to-Real Domain Adaptation
Department of Computer Science University of Kentucky Lexington, KY 40506
A Systematic Evaluation of Current Architectures in Wind Power Forecasting
Jun 1, 2026Vinicius Bortolini, Gilson Adamczuk Oliveira, Erick Oliveira Rodrigues +1Time Series Forecasting Wind Power Forecasting
Industrial and Systems Engineering Graduate Program (PPGEPS), Federal University of Technology Parand (UTFPR), Pato Branco, Parand $5503-390, Brazil
Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins
Jun 1, 2026Taye Akinrele, James Halgren, Noorbakhsh Amiri Golilarz +2Transformer Time Series Forecasting
Dept. of Computer Science, The University of Alabama, Tuscaloosa, USA · Alabama Water Institute, USA
Bridging the Last Mile of Time Series Forecasting with LLM Agents
Jun 1, 2026Yuhua Liao, Zetian Wang, Qiangqiang Nie +1Time Series Forecasting LLM-Based Time Series Forecasting
Trip.com Group, Shanghai, China
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
Jun 1, 2026Zhensheng Wang, Xiaole Liu, Wenmian Yang +3Table QA Time Series Reasoning
School of Artificial Intelligence, Beijing Normal University, Beijing, PR China · Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, PR China · Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai, PR China +1
Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction
Jun 1, 2026Ruiyuan Li, Ajay Seth, Manon KokTime Series Forecasting Recurrent Neural Networks
Delft Center for Systems and Control, TU Delft, the Netherlands · Department of Biomechanical Engineering, TU Delft, the Netherlands
VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
Jun 1, 2026Xudong Zhang, Jierui Lei, Jiacheng Li +3Multivariate Time Series Forecasting Latent Variable Models
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China. · Center for Machine Learning Research, Peking University, Beijing, China. · Amap, Alibaba Group, China. +2
Why Do Time Series Models Need Long Context Windows?
Jun 1, 2026Luca Butera, Giovanni De Felice, Andrea Cini +1Time Series Forecasting Long-Context Modeling
1Università della Svizzera Italiana · 2EPFL · 3Politecnico di Milano
FreqLite: A Lightweight Frequency-Decomposed Linear Model with Adaptive Reversible Normalization for Robust Long-Term Time-Series Forecasting
May 31, 2026Mirza Samad Ahmed Baig, Syeda Anshrah GillaniLong-Term Time Series Forecasting Frequency-Domain Feature Learning
Fandaqah, Al Khobar, Saudi Arabia · Hamdard University, Karachi, Pakistan
Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting
May 31, 2026Yifan Wu, Junjie Wu, Kai Wu +2Zero-Shot Time Series Forecasting Time Series Forecasting
School of Artificial Intelligence, Xidian University, Xi’an, China · School of Cyber Engineering, Xidian University, Xi’an, China · Sun Yat-Sen University, Guangzhou, China
Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing
May 30, 2026T. Ansah-Narh, Y. Asare Afrane, J. Bremang TandohEpidemic Forecasting Time Series Forecasting
Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler
May 30, 2026T. Ansah-Narh, Y. Asare Afrane, J. Bremang TandohEpidemic Forecasting Bayesian Inference
Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, P. O. Box LG 80, Legon, Accra, Ghana · Department of Medical Microbiology, University of Ghana Medical School, University of Ghana, Accra, Ghana
EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
May 30, 2026Dahai Yu, Rongchao Xu, Lin Jiang +1Selective SSMs Time Series Forecasting
Florida State University · Tallahassee, Florida, USA
Generalizing Multi-Scale Time-Series Modeling with a Single Operator
May 29, 2026Cheonwoo Lee, Dooho Lee, Doyun Choi +1Long-Term Time Series Forecasting Time Series Forecasting
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea · Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
STEP: Learning STructured Embeddings for Progressive Time Series
May 29, 2026Lucas Thil, Jesse Read, Rim Kaddah +1Self-Supervised Time Series Representation Learning Interpretable ML
LIX, École Polytechnique IRT SystemX Palaiseau, France · IRT SystemX Palaiseau, France · Safran Tech Chateaufort, France
Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble
May 29, 2026Sahaj Raj MallaLong-Term Time Series Forecasting Time Series Forecasting
Department of Mathematics, Kathmandu University, Dhulikhel, Nepal
Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learning
May 28, 2026Yujin Jeong, Noelle Jung, Brian Y. C. LeungSentence Embeddings Time Series Forecasting
Mike
KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
May 28, 2026Kun Feng, Ziwei Shan, Yuchen Fang +6Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
ShanghaiTech University · Ant Group
From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting
May 28, 2026Zesheng Liu, Maryam RahnemoonfarNeural Surrogate Modeling Long-Term Time Series Forecasting
Department of Computer Science and Engineering, Lehigh University, Bethlehem PA 18015, USA · Department of Civil and Environmental Engineering, Lehigh University, Bethlehem2026 PA 18015, USA

Jun 3, 2026J/K move · Enter open · S save

RIDE: An Open Dataset and Benchmark for Train Delay Prediction

Clément Elliker, Mathis Le Bail, Clément Mantoux, Jesse Read+1

LIX, École Polytechnique, IP Paris, France · e.SNCF Solutions, France

Intelligent Transportation Systems Time Series Forecasting Forecasting Benchmarks

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Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols. To address this gap, we introduce RIDE, an open dataset and benchmark for train delay prediction built at nationwide scale over the Belgian railway network. RIDE covers 94.5M train events, 3.6M journeys, and 35.7M weather records from 2023 to 2025. It is organized as a layered data pipeline from raw railway and weather sources to two public releases: a reusable intermediate relational dataset and model-ready benchmark datasets. The benchmark standardizes the prediction task and the training and testing data. It also provides a unified evaluation protocol that supports direct comparison across models. Using this framework, we provide the first comprehensive comparative evaluation of non-learning, statistical learning, and deep learning models. We show that learning-based methods clearly outperform non-learning models, with graph neural networks achieving the best mean performance, while the strongest learning-based models remain relatively close to one another. Beyond aggregate mean absolute error (MAE) and root mean squared error (RMSE), the framework also provides breakdowns by prediction horizon and delay change, enabling more detailed analysis of model behavior across forecasting regimes.