Modeling Latent Non-Linear Dynamical System over Time Series
This work addresses a gap in modeling long-term temporal dependencies in time series data, which is an incremental improvement for researchers in dynamical systems and time series analysis.
The paper tackles the problem of modeling non-linear dynamical systems from time series data by introducing latent states and a novel method called LaNoLem, which achieves competitive performance in dynamics estimation and outperforms other methods in prediction.
We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing studies. In this paper, we introduce a latent state to allow time-dependent modeling and formulate this problem as a dynamics estimation problem in latent states. We face multiple technical challenges, including (1) modeling latent non-linear dynamics and (2) solving circular dependencies caused by the presence of latent states. To tackle these challenging problems, we propose a new method, Latent Non-Linear equation modeling (LaNoLem), that can model a latent non-linear dynamical system and a novel alternating minimization algorithm for effectively estimating latent states and model parameters. In addition, we introduce criteria to control model complexity without human intervention. Compared with the state-of-the-art model, LaNoLem achieves competitive performance for estimating dynamics while outperforming other methods in prediction.