Neural Bilinear Dynamical Models Enhance Nonlinear Time Series Forecasting
A new study has introduced the Neural Bilinear Dynamical Model (NBDM) to tackle the limitations of existing time series forecasting methods, which often rely on linear models or Koopman linearizations. This research, available on arXiv under the ID 2608.04471, points out that many time series arise from nonlinear systems, making long-term predictions tricky. The NBDM effectively captures these nonlinear dynamics by using a bilinear latent framework, applying Koopman theory to transition nonlinear dynamics into a higher-dimensional space where state changes are modeled bilinearly. To mitigate errors from these approximations, the authors included a parameterized error compensation term, also incorporating control inputs. This work, categorized as a cross-type announcement, aims to enhance forecasting accuracy in complex systems through improved machine learning techniques.
Key facts
- The paper proposes the Neural Bilinear Dynamical Model (NBDM) for time series forecasting.
- It addresses limitations of linear and Koopman-based linearization methods.
- NBDM uses a bilinear latent dynamical formulation to model nonlinear dynamics.
- Koopman theory is used to lift dynamics into a higher-dimensional latent space.
- A parameterized error compensation term is included to reduce approximation error.
- Control inputs are explicitly integrated into the model.
- The paper is available on arXiv with identifier 2608.04471.
- The announcement type is 'cross', indicating potential prior publication.
Entities
Institutions
- arXiv