Chaotic Time-Series Prediction

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Overview

Chaotic time-series prediction asks how to infer a system’s evolving state and useful long-run statistics from finite, noisy observations, in settings such as Lorenz dynamics, turbulence, and climate. Common approaches include reservoir computing and recurrent or transformer models, alongside Neural ODEs, Koopman operator learning, and physics-informed or structure-preserving surrogates; symbolic regression seeks interpretable equations. Because small errors grow quickly, evaluation distinguishes short-term trajectory skill from stable rollouts, long-run statistics, and rare-event forecasts, using benchmarks that vary noise, chaos level, and forecast horizon. The central tension is that exact trajectories become unreliable beyond Lyapunov-limited horizons, while models that match long-run statistics may miss event timing or physical mechanisms; robust uncertainty estimates and data-efficient generalization remain open challenges.

Latest papers

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54 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

  1. Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting

    May 27, 2026Shadmehr Zaregarizi, Khashayar YavariDynamic Neural NetworksForecasting Benchmarks

    Politecnico di Torino Turin, Italy

  2. A comparative study of accuracy and rollout stability of temporal surrogate models

    May 24, 2026Rajarshi BiswasNeural Surrogate ModelingLong-Term Time Series Forecasting

    Cargill Inc. Wayzata, MN 55391, USA

  3. FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series

    May 17, 2026Andrei Velichko, N'Gbo N'Gbo, Bruno Carpentieri +1Time Series ForecastingChaotic Dynamical Systems

    Institute of Physics and Technology, Petrozavodsk State University, Petrozavodsk, Russia · School of Science and Engineering, International University of Grand Bassam, Grand Bassam, Cote d’Ivoire · Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy +2

  4. QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction

    May 14, 2026Emir Kaan ÖzdemirHybrid Quantum-Classical MLSpatiotemporal Forecasting

    Istanbul Erkek High School Ministry of National Education Istanbul, Türkiye · Ministry of National Education

  5. ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics

    May 10, 2026Henok Tenaw Moges, Charalampos Skokos, Deshendran MoodleyForecasting BenchmarksSynthetic Benchmark Generation

    Centre for Artificial Intelligence Research (CAIR) University of Cape Town, Cape Town, South Africa · Nonlinear Dynamics and Chaos Group, Department of Mathematics and Applied Mathematics University of Cape Town, Cape Town, South Africa

  6. Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamics

    Apr 28, 2026Andre Herz, Daniel Durstewitz, Georgia KoppeNeural Surrogate ModelingGeneralized Bayesian Inference

    Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany · Dept. of Theoretical Neuroscience, Central Institute of Mental Health (CIMH), Mannheim, Germany · Hector Institute for AI in Psychiatry and Dept. of Psychiatry and Psychotherapy, CIMH, Mannheim, Germany +1

  7. An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor

    Apr 26, 2026Tushar PandeyQuantum Circuit ExpressibilityVariational Quantum Circuits

    Texas A&M University, College Station, TX 77843, USA

  8. Neuromorphic Computing Based on Parametrically-Driven Oscillators and Frequency Combs

    Apr 23, 2026Mahadev Sunil Kumar, Adarsh GanesanDynamical SystemsReservoir Computing

    Amrita School of Computing, Amrita Vishwa Vidyapeetham, Kollam, India 690525 · Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani – Dubai Campus, Dubai, United Arab Emirates 345055

  9. A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting

    Apr 22, 2026Brooks Kinch, Xiaozhe Hu, Yilong Huang +6Neural Surrogate ModelingTime Series Transformers

    Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA · Department of Mathematics, Tufts University, Medford, MA, USA · Department of Mathematics and Cybernetics, SINTEF Digital, Oslo, Norway +3

  10. Learning to Emulate Chaos: Adversarial Optimal Transport Regularization

    Apr 22, 2026Gabriel Melo, Leonardo Santiago, Peter Y. LuNeural Surrogate ModelingChaotic Dynamical Systems

    LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau · Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC · University of Campinas +1

  11. Horizon-Constrained Rashomon Sets for Chaotic Forecasting

    Apr 17, 2026Gauri Kale, Rahul Vishwakarma, Holly Diamond +2Chaotic Dynamical SystemsChaotic Time-Series Prediction

    Dept. of Electrical Engineering, California State University Long Beach, USA · WorkOnward Inc., USA · Dept. of Computer Engineering & Computer Science, California State University Long Beach, USA

  12. Recurrent Quantum Feature Maps for Reservoir Computing

    Apr 3, 2026Utkarsh Singh, Aaron Z. Goldberg, Christoph Simon +1Quantum Reservoir ComputingQuantum Feature Maps

    Department of Physics, University of Ottawa, 25 Templeton Street, Ottawa, Ontario, K1N 6N5 Canada · National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1N 5A2, Canada · Institute for Quantum Science and Technology, Department of Physics and Astronomy, University of Calgary, Alberta T2N 1N4, Canada +1

  13. Noise Titration: Exact Distributional Benchmarking for Probabilistic Time Series Forecasting

    Mar 23, 2026Qilin WangProbabilistic Time Series ForecastingForecasting Benchmarks

    Independent Researcher

  14. Storage and selection of multiple chaotic attractors in minimal reservoir computers

    Mar 16, 2026Francesco Martinuzzi, Holger KantzReservoir ComputingDynamical Systems

    Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

  15. A Score Filter Enhanced Data Assimilation Framework for Data-Driven Dynamical Systems

    Mar 16, 2026Jingqiao Tang, Ryan Bausback, Feng Bao +2Data AssimilationScientific ML

  16. Hierarchy of extreme-event predictability in turbulence revealed by machine learning

    Mar 14, 2026Yuxuan Yang, Chenyu Dong, Gianmarco MengaldoChaotic Time-Series PredictionComputational Fluid Dynamics

    National University of Singapore Singapore

  17. Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems

    Mar 13, 2026Dibyajyoti Chakraborty, Hojin Kim, Romit MaulikPDE Surrogate ModelingChaotic Time-Series Prediction

    College of Information Sciences and Technology, Pennsylvania State University, University Park, 16802, PA, USA · School of Mechanical Engineering, Purdue University, West Lafayette, 47907, IN, USA

  18. Dynamics-Informed Deep Learning for Predicting Extreme Events

    Mar 11, 2026Eirini Katsidoniotaki, Themistoklis P. SapsisChaotic Time-Series PredictionChaotic Dynamical Systems

    Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139

  19. Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series

    Mar 7, 2026Madhurima Panja, Grace Younes, Tanujit ChakrabortyTime Series ForecastingChaotic Time-Series Prediction

    SAFIR, Sorbonne University Abu Dhabi, UAE · Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, France

  20. Adaptive Sensing of Continuous Physical Systems for Machine Learning

    Mar 4, 2026Felix Köster, Atsushi UchidaAdaptive SensingActive Perception

    Department of Information and Computer Sciences, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama, 338–8570, Japan

  21. A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

    Nov 10, 2025Xuyang Li, John Harlim, Dibyajyoti Chakraborty +1Nonlinear System IdentificationNeural ODEs

    aSchool of Construction, University of North Carolina at Charlotte, Charlotte, 28223, NC, USA · cDepartment of Mathematics, Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, 16802, PA, USA · bCollege of Information Sciences and Technology, The Pennsylvania State University, University Park, 16802, PA, USA +1

  22. Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics

    Jul 21, 2025Alex Guo, Michael D. GrahamReduced-Order ModelingChaotic Time-Series Prediction

    Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706, USA

  23. Tailored minimal reservoir computing: on the bidirectional connection between nonlinearities in the reservoir and in data

    Apr 24, 2025Davide Prosperino, Haochun Ma, Christoph RäthNonlinear System IdentificationReservoir Computing

    Ludwig-Maximilians-Universität München, Faculty of Physics, Geschwister-Scholl-Platz 1, 80539 Munich, Germany · Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institute for Frontier Materials on Earth and in Space, Linder Höhe, 51147 Cologne, Germany

  24. Adversarial dynamical systems characterize when data-driven learning succeeds or fails

    Jul 8, 2024Matthew J. Colbrook, Igor Mezić, Alexei StepanenkoKoopman Operator LearningSpectral Methods

    DAMTP, University of Cambridge, Cambridge, CB3 0WA, UK. · University of California, Santa Barbara, CA 93106, USA.