Statistical learning of nonlinear stochastic differential equations from non-stationary time series using variational clustering

TL;DR

This paper introduces a variational clustering method for estimating parameters of nonlinear, non-stationary stochastic differential equations from time series data, enabling the recovery of hidden parameter dynamics.

Contribution

It develops a novel combination of quadratic programming and Hermite expansion-based likelihood for non-stationary SDE parameter estimation with clustering.

Findings

Successfully recovers hidden functional relationships between parameters and auxiliary processes.

Demonstrates the approach's effectiveness on numerical examples.

Provides a framework for data-driven non-stationary stochastic modeling.

Abstract

Parameter estimation for non-stationary stochastic differential equations (SDE) with an arbitrary nonlinear drift, and nonlinear diffusion is accomplished in combination with a non-parametric clustering methodology. Such a model-based clustering approach includes a quadratic programming (QP) problem with equality and inequality constraints. We couple the QP problem to a closed-form likelihood function approach based on suitable Hermite expansion to approximate the parameter values of the SDE model. The classification problem provides a smooth indicator function, which enables us to recover the underlying temporal parameter modulation of the one-dimensional SDE. The numerical examples show that the clustering approach recovers a hidden functional relationship between the SDE model parameters and an additional auxiliary process. The study builds upon this functional relationship to develop closed-form, non-stationary, data-driven stochastic models for multiscale dynamical systems in real-world applications.