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. Extrapolating learned dynamics across bifurcations based on generalized synchronization

    Oct 25, 2026Lingfeng Chen, Masanobu InubushiDynamical SystemsChaotic Time-Series Prediction

    Department of Applied Mathematics, Tokyo University of Science, 1-3, Kagurazaka, Shinjuku, Tokyo 162-8601, Japan · Graduate School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan

  2. Spatial Context or Memory? Sizing the Inputs of Neural Surrogates for Partially Observed Fields

    Oct 8, 2026Ruige KongNeural Surrogate ModelingSpatiotemporal Forecasting

    University of Washington, Seattle, WA, USA

  3. The surprising simplicity of forecasting quantum chaotic many-body dynamics

    Oct 7, 2026Francesco Perciavalle, Agostino Gallo, Francesco Plastina +3Chaotic Time-Series PredictionTime Series Forecasting

    Dipartimento di Fisica, Universit`a della Calabria, Rende 87036, Italy. · INFN, Sezione LNF, gruppo collegato di Cosenza. · Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy.

  4. Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics

    Oct 2, 2026Lufang Chiang, Jiachen Yao, Thomas Y. L. Lin +1Chaotic Time-Series PredictionChaotic Dynamical Systems

    National Taiwan University · California Institute of Technology · University of Washington

  5. Learning Chaos Without Seeing Chaos: Extrapolation of Global Dynamics in Autoregressive Transformers

    Sep 30, 2026Yilun Liu, Yi Zhang, Ganyu Wu +5Dynamical SystemsChaotic Dynamical Systems

    Ludwig Maximilian University of Munich · Munich Center for Machine Learning · Technical University of Munich

  6. Context-dependent time-series prediction via HyperReservoirs

    Sep 28, 2026Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki +1Time Series ForecastingReservoir Computing

    Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

  7. STRIDE: State-Transition Representation via Increment Dynamics and Evolution

    Sep 26, 2026Yuchen Xiong, Siming Huang, Jianfeng SunTime Series ForecastingProbabilistic Time Series Forecasting

  8. Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

    Sep 25, 2026Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis +1Rare-Event EstimationChaotic Time-Series Prediction

    Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge, MA 02139 · Department of Applied Mathematics and Statistics, Johns Hopkins University Baltimore, MD 21218 · Department of Chemical and Biomolecular Engineering, Johns Hopkins University Baltimore, MD 21218

  9. Prediction of Nonlinear Oscillations in a Jumping Quarter-Car Model Using Reservoir Computing

    Sep 1, 2026Masahisa Watanabe, Shiva Dixit, Nirmal Punetha +2Chaotic Time-Series PredictionVehicle Dynamics

    Division of Environmental and Agricultural Engineering, Institute of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan · Amity Institute of Integrative Sciences and Health, Amity University Haryana - Gurugram, Manesar, 122 413 Haryana, India · Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466 8555, Japan +1

  10. Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Series Forecasting under Short-term Observations

    Aug 30, 2026Yuhang Yao, Bohan JiangLong-Term Time Series ForecastingChaotic Time-Series Prediction

    Nanjing University of Posts and Telecommunications Nanjing, China · Arizona State University Tempe, Arizona, USA

  11. Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?

    Aug 26, 2026Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun +4Weather ForecastingChaotic Time-Series Prediction

    Department of Geophysical Sciences, University of Chicago, Chicago, USA · Committee on Computational and Applied Mathematics, University of Chicago, Chicago, USA. · Data Science Institute, University of Chicago, Chicago, USA. +3

  12. Rollout-Decoded Reconstruction for Long-Horizon Prediction in Latent World Models

    Aug 25, 2026Rishi Shah, Rishav ShresthaLatent World ModelsLatent Dynamics Modeling

    E3A Healthcare

  13. Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting

    Aug 24, 2026Krishna Bhatia, Harsh, Shalini DevendrababuQuantum Reservoir ComputingReduced-Order Modeling

    QuantumAI Lab, Fractal Analytics, Mumbai, India · Department of Physics, Indian Institute of Technology, Jodhpur, India

  14. Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems

    Aug 23, 2026Zhou Fang, Gianmarco MengaldoChaotic Time-Series PredictionAdaptive Loss Weighting

    Department of Mechanical Engineering National University of Singapore Singapore

  15. Symbolic Neural ODEs: Learning interpretable models from time-series data

    Aug 22, 2026Nibodh Boddupalli, Jeff MoehlisNonlinear System IdentificationSystem Identification

    Dept. of Mechanical Engineering, University of California, Santa Barbara, CA 93106

  16. Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics

    Aug 20, 2026Andrei Velichko, Viet-Thanh PhamRare-Event PredictionChaotic Time-Series Prediction

    Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., Petrozavodsk, 185910, Russia · Faculty of Electronics Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

  17. Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics

    Aug 17, 2026Conrad Ainslie, Pedram Hassanzadeh, Michael W. Mahoney +1Neural Network StabilityNeural Surrogate Modeling

    Department of Applied Mathematics, University of California, Santa Cruz, Santa Cruz, 95064, CA · Department of Geophysical Sciences, University of Chicago, Chicago, IL 60637 · Committee on Computational and Applied Mathematics, University of Chicago, Chicago, IL 60637 +3

  18. On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems

    Jul 30, 2026Illia HorenkoScientific MLFeature Selection

    Chair for Mathematics of AI, Faculty of Mathematics, RPTU Kaiserslautern-Landau, Gottlieb-Daimler-Str. 48, Kaiserslautern, 67663, Germany.

  19. fSRD: Fuzzy Spectral Region Decomposition -- Automated Multi Operator Koopman Representations via an Adaptive Spectral Learning Architecture

    Jul 20, 2026Charles Bokor, Mark Cary, Denise Morrey +1Koopman Operator LearningNonlinear System Identification

    School of Engineering, Computing and Mathematics Oxford Brookes University Oxford OX33 1HX, UK · Department of Aeronautical and Automotive Engineering Loughborough University Loughborough LE11 3TU, UK

  20. Beyond the Edge of Chaos: Stability-Expressivity Transfer in Reservoir Forecasting

    Jul 20, 2026Yao Du, Xingang WangLyapunov StabilityReservoir Computing

    School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062, China

  21. A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps

    Jul 8, 2026Tushar PandeyReservoir ComputingQuantum Reservoir Computing

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

  22. The Fractal Neural Operator: Overcoming Spectral Bias in Chaotic Attractors via Prime-Harmonic Weierstrass Encodings

    Jun 22, 2026Kanishk AwadhiyaSpectral BiasChaotic Dynamical Systems

    Independent Researcher

  23. Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos

    Jun 22, 2026Nima DehghaniEvolutionary OptimizationReservoir Computing

    Department of Physics, MIT · McGovern Institute for Brain Research, MIT

  24. LSTM Variants for Chaotic Dynamical Systems: An Empirical Study on the Lorenz Attractor

    Jun 21, 2026Ruslan GokhmanTemporal Convolutional NetworksChaotic Dynamical Systems

    Yeshiva University

  25. Neural dynamical systems on ferroelectric compute-in-memory for real-time forecasting

    Jun 15, 2026Keshava Katti, Adithya Selvakumar, Pratik Chaudhari +1Compute-in-MemoryEfficient Neural Network Inference

    Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA

  26. CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting

    Jun 14, 2026Abdul Joseph Fofanah, Lian Wen, David Chen +1Meta-LearningFew-Shot Learning

    School of Information and Communication Technology, Griffith University, Brisbane, 4111, Australia · School of Information Engineering, Chang’an University, Xi’an, China

  27. Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark

    Jun 8, 2026Shundong LiForecasting BenchmarksChaotic Dynamical Systems

    Worcester Polytechnic Institute Worcester, MA, USA

  28. Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge

    Jun 2, 2026Cen LuTrajectory ReconstructionTime Series Forecasting

    EPFL & Idiap Research Institute · Switzerland

  29. Learning Chaotic Dynamics through Second-Order Geometric Supervision

    Jun 1, 2026Shinhoo Kang, Hai V. Nguyen, Tan Bui-ThanhNonlinear System IdentificationChaotic Dynamical Systems

    Department of Computer Science and Software Engineering, Korea University, Sejong 30019, KR · Department of Aerospace Engineering and Engineering Mechanics, The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, 2026 TX, USA

  30. Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error

    May 29, 2026Markus GrossKoopman Operator LearningNonlinear System Identification

    Institute for AI Safety and Security, German Aerospace Center (DLR), Sankt Augustin and Ulm, Germany