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.

MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
Jun 27, 2026Andrea Carriero, Davide Pettenuzzo, Shubhranshu ShekharData Leakage Time Series Forecasting
School of Economics and Finance, Queen Mary University of London. · School of Business and Economics, Brandeis University.
Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion
Jun 26, 2026Ruchi PakhleTime Series Forecasting Time Series Transformers
Data and AI · Red Hat
NIVA: A Multimodal Foundation Model for Actionable Earth System Intelligence
Jun 26, 2026Anisha Pal, Aodhan Sweeney, Kyle Heyblom +1Climate Science Multimodal Pretraining
Independent Researcher · Work done while at Planette AI · Planette AI, USA.
The Remittance Blueprint: Data-driven Intelligence for Sri Lanka
Jun 26, 2026Dhinanjaya Fernando, Dinura Ginige, Kalana Lakshan +6Multivariate Time Series Forecasting Time Series Forecasting
Dept. of Computer Science & Engineering, University of Moratuwa, Sri Lanka.
GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets
Jun 26, 2026Janak M. Patel, Anirudh Deodhar, Dagnachew BirruMultivariate Time Series Forecasting Long-Term Time Series Forecasting
Phi Labs, Quantiphi Marlborough, MA 01752, USA
The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference
Jun 26, 2026Pablo Montero-Manso, Marcel ScharthSimulation-Based Inference Zero-Shot Time Series Forecasting
University of Sydney Business School, Australia
Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting
Jun 26, 2026Riku Green, Zahraa S. Abdallah, Telmo M Silva FilhoFinancial Forecasting Time Series Forecasting
The University of Bristol Bristol, UK
Global Explanations for Multivariate Time Series Forecasting Models via $K$ -Order Markov Approximations
Jun 25, 2026Amadeo TunyiMultivariate Time Series Forecasting Surrogate Modeling
XITASO GmbH Austraße 35, 86153 Augsburg, Germany
How Good Can Linear Models Be for Time-Series Forecasting?
Jun 25, 2026Lang Huang, Jinglue Xu, Luke DarlowTime Series Forecasting Hyperparameter Optimization
1Sakana AI, Tokyo, Japan · 2National Institute of Informatics, Japan
PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting
Jun 25, 2026Ao Hu, Liangjian Wen, Jiang Duan +7Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Southwestern University of Finance and Economics · Artificial Intelligence and Digital Finance Key Laboratory of Sichuan Province · Chengdu Everimaging Science and Technology Co., Ltd. +4
Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting
Jun 25, 2026Johnny Peng, Thanh Tung Khuat, Ellen Otte +2Irregular Time-Series Modeling Time Series Forecasting
Complex Adaptive Systems Laboratory, The Data Science Institute, University of Technology Sydney, NSW 2007, Australia · CSL Innovation, Melbourne, VIC 3000, Australia
Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting
Jun 25, 2026Defu Cao, Zijie Lei, Muyan Weng +2Time Series Forecasting LLM-Based Time Series Forecasting
University of Southern California · Meta · Google DeepMind
Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
Jun 24, 2026Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya +4Time Series Forecasting Probabilistic Forecasting
University of Cambridge
Sampling sea state using a diffusion model
Jun 24, 2026Jiarong Wu, Bertrand Chapron, Laure ZannaTime Series Forecasting Probabilistic Forecasting
Courant Institute School of Mathematics, Computing and Data Science, New York University, 251 Mercer Street, New York, 10012, NY, USA. · Laboratoire d’Oc´eanographie Physique et Spatiale (LOPS), Ifremer, 1625 route de Sainte-Anne, Plouzan´e, 29280, Bretagne, France.
Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
Jun 24, 2026Yoonsik Hong, Diego KlabjanQuantitative Finance Time Series Forecasting
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, 60208, IL, USA
Cellular Predictions on the Move: What about Data?
Jun 24, 2026Natalia Vesselinova, Pauliina IlmonenTime Series Forecasting Traffic Forecasting
TopoCast: A Topological Fidelity Framework for Evaluating Transformer-Based Time Series Forecasting
Jun 24, 2026Sandeepa Weerasekara, Sandareka WickramanayakeTime Series Forecasting Time Series Transformers
Department of Computer Science and Engineering, University of Moratuwa, Katubedda, Moratuwa, 10400, Sri Lanka.
FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
Jun 23, 2026Nicholas Majeske, Ariful AzadExplainable Time Series Forecasting Time Series Forecasting
Department of Intelligent Systems Engineering, Indiana University Bloomington, USA · Department of Computer Science and Engineering, Texas A&M University, USA
Adapt Only When It Pays: Budgeted Decision-Loss Priority for Delayed Online Time-Series Adaptation
Jun 23, 2026Xibai WangTest-Time Adaptation Continual Test-Time Adaptation
NeuroQuant Labs Limited Hong Kong SAR, China
From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol
Jun 23, 2026Geumyoung KimTime Series Forecasting Forecasting Benchmarks
Chungbuk National University
Model selection with proper scoring rules on data sets of time series: prefer the mean scaled score
Jun 23, 2026Giorgio Corani, Stefano Damato, Dario Azzimonti +1Model Selection Time Series Forecasting
SUPSI, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland
Frequency Domain Reservoir Computing
Jun 23, 2026Klaus Schertler, Xiomara Runge, Andrea Ceni +2Time Series Forecasting Recurrent Neural Networks
Airbus Central Research & Technology Taufkirchen, Germany · Department of Computer Science University of Pisa, Italy · Faculty of Technology Bielefeld University, Germany
Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series
Jun 23, 2026Yujiang He, Frederic Uhrweiller, Bernhard SickContinual Learning Time Series Forecasting
Intelligent Embedded Systems, University of Kassel, 34121 Kassel, Germany
MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios
Jun 23, 2026Patara Trirat, Jin Myung Kwak, Jay Heo +2Quantitative Finance Financial Forecasting
KAIST
RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting
Jun 23, 2026Cheng He, Zhenyu Guan, Xijie Liang +6Financial Forecasting Time Series Forecasting
University of Science and Technology of China, China · Shanghai Black Wing Asset Management Co., Ltd., China · College of Sciences, Shanghai University, China +2
Selective Time Series Forecasting via Metalearning
Jun 22, 2026Ricardo Inácio, Vitor Cerqueira, Marília Barandas +1Selective Prediction Time Series Forecasting
Faculdade de Engenharia da Universidade do Porto, Porto, Portugal · University of Coimbra, Portugal · Fraunhofer Portugal AICOS, Portugal
Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
Jun 22, 2026Jinhao Li, Hao WangMultivariate Time Series Forecasting Time Series Forecasting
Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia · Monash Energy Institute, Monash University, Melbourne, Victoria, Australia
Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Jun 22, 2026Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng +8Time Series Forecasting Quantum Machine Learning
Wells Fargo · Stevens Institute of Technology · National Taiwan University +3
A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis
Jun 22, 2026Ye Yuan, Song Wang, Hongxun Zhou +2Time Series Forecasting Cloud Computing
College of Computer and Information Science, Southwest University, Chongqing 400715, China
Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models
Jun 21, 2026Attia Qammar, Qazi Haseeb Yousaf, Ali Azam +3Time Series Forecasting Federated Learning
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 610031, China · Department of Computer Science, Bahria University, Islamabad · School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, PR China

Jun 27, 2026J/K move · Enter open · S save

MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting

Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar

School of Economics and Finance, Queen Mary University of London. · School of Business and Economics, Brandeis University.

Data Leakage Time Series Forecasting Temporal Data Leakage Time-Series Foundation Models

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We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting. Existing TSFMs suffer from data leakage in two forms: temporal contamination, as the model may have seen the realized values of the series it forecasts, and revision bias, as training on fully revised data diverges from the preliminary, vintage-specific releases available to real-time forecasters. MACROCAST is, to our knowledge, the first TSFM that rules out both forms of leakage entirely: at no stage of training is the model exposed to information that would not have been available to a forecaster in real time. We train MACROCAST first on purely synthetic time series in approximately one GPU-day and then fine-tune it on synthetic time series drawn from Bayesian VARs, dynamic factor models, and ARIMA specifications estimated on vintage-specific ALFRED data. Because pretraining uses only simulated data and fine-tuning uses only real-time vintages, no observed future or revised value ever enters the model; each fine-tuning run takes nine minutes. Evaluated on the FRED-MD database in a genuine real-time out-of-sample exercise, MACROCAST improves on the AR(1) benchmark for roughly 80% of series-horizon pairs, matches or surpasses Chronos-2 -- the strongest currently available TSFM -- and outperforms the Bayesian VAR and dynamic factor model benchmarks, all in a data-leakage-free manner.