Parallel BiLSTM-Transformer networks for forecasting chaotic dynamics

• URL: http://arxiv.org/abs/2510.23685v1
• Date: Mon, 27 Oct 2025 16:17:10 GMT
• Title: Parallel BiLSTM-Transformer networks for forecasting chaotic dynamics
• Authors: Junwen Ma, Mingyu Ge, Yisen Wang, Yong Zhang, Weicheng Fu,
• Abstract summary: This study proposes a parallel predictive framework integrating Transformer and Bidirectional Long Short-Term Memory networks.<n>The proposed hybrid model employs a dual-branch architecture, where the Transformer branch mainly captures long-range dependencies.<n>The results consistently indicate that the proposed hybrid framework outperforms both single-branch architectures across tasks.
• Score: 24.960864709838436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nonlinear nature of chaotic systems results in extreme sensitivity to initial conditions and highly intricate dynamical behaviors, posing fundamental challenges for accurately predicting their evolution. To overcome the limitation that conventional approaches fail to capture both local features and global dependencies in chaotic time series simultaneously, this study proposes a parallel predictive framework integrating Transformer and Bidirectional Long Short-Term Memory (BiLSTM) networks. The hybrid model employs a dual-branch architecture, where the Transformer branch mainly captures long-range dependencies while the BiLSTM branch focuses on extracting local temporal features. The complementary representations from the two branches are fused in a dedicated feature-fusion layer to enhance predictive accuracy. As illustrating examples, the model's performance is systematically evaluated on two representative tasks in the Lorenz system. The first is autonomous evolution prediction, in which the model recursively extrapolates system trajectories from the time-delay embeddings of the state vector to evaluate long-term tracking accuracy and stability. The second is inference of unmeasured variable, where the model reconstructs the unobserved states from the time-delay embeddings of partial observations to assess its state-completion capability. The results consistently indicate that the proposed hybrid framework outperforms both single-branch architectures across tasks, demonstrating its robustness and effectiveness in chaotic system prediction.

Related papers

• FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier State Space Integration [21.39395366378851]
  We present FoSS, a dual-branch framework that unifies frequency-domain reasoning with linear-time sequence modeling.<n>Experiments on Argoverse 1 and Argoverse 2 benchmarks demonstrate that FoSS achieves state-of-the-art accuracy while reducing computation by 22.5% and parameters by over 40%.
  arXiv  Detail & Related papers  (2026-03-01T21:38:59Z)
• KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra [65.11254608352982]
  We introduce a generator-based neural Koopman framework that models dynamics through a structured, state-dependent representation of Koopman generators.<n>By exploiting the intrinsic Cartesian decomposition into skew-adjoint and self-adjoint components, KoopGen separates conservative transport from irreversible dissipation.
  arXiv  Detail & Related papers  (2026-02-15T06:32:23Z)
• Transformer with Koopman-Enhanced Graph Convolutional Network for Spatiotemporal Dynamics Forecasting [12.301897782320967]
  TK-GCN is a two-stage framework that integrates geometry-aware spatial encoding with long-range temporal modeling.<n>We show that TK-GCN consistently delivers superior predictive accuracy across a range of forecast horizons.
  arXiv  Detail & Related papers  (2025-07-05T01:26:03Z)
• Topology-Aware Conformal Prediction for Stream Networks [54.505880918607296]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework.<n>Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling [3.8719670789415925]
  Recurrent configuration networks (RSCNs) have shown great potential in modelling nonlinear dynamic systems with uncertainties.<n>This paper presents an RSCN with hybrid regularization to enhance both the learning capacity and generalization performance of the network.
  arXiv  Detail & Related papers  (2024-11-26T03:06:39Z)
• A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics [51.147876395589925]
  A non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time. A fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance.
  arXiv  Detail & Related papers  (2024-02-26T04:39:01Z)
• Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction [1.8531577178922987]
  We propose a model that combines hybrid Transformer and self-supervised learning. The model enhances its adaptive data augmentation by applying data augmentation techniques at the sequence-level of the traffic. We design two self-supervised learning tasks to model the temporal and spatial dependencies, thereby improving the accuracy and ability of the model.
  arXiv  Detail & Related papers  (2024-01-29T06:17:23Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection [0.0]
  We present an end-to-end differentiable neural network architecture to perform anomaly detection in multivariate time series. The architecture is a cascade of dynamical systems designed to separate linearly predictable components of the signal. The anomaly detector exploits the temporal structure of the prediction residuals to detect both isolated point anomalies and set-point changes.
  arXiv  Detail & Related papers  (2022-02-25T01:50:22Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Feedback-induced instabilities and dynamics in the Jaynes-Cummings model [62.997667081978825]
  We investigate the coherence and steady-state properties of the Jaynes-Cummings model subjected to time-delayed coherent feedback. The introduced feedback qualitatively modifies the dynamical response and steady-state quantum properties of the system.
  arXiv  Detail & Related papers  (2020-06-20T10:07:01Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.