An Information-Flow-Based Sensitivity Analysis Method for Continuous-Time Models

Abstract

Sensitivity analysis plays an important role in analyzing most influential factors of models. There are few studies on sensitivity analysis (SA) of continuous time models. By using the concept of information flow, this paper presents a new sensitivity analysis method for dynamical models. By combining the direction and magnitude of information transfer, the method builds a novel framework for SA, with the rigorous mathematical and statistic theories on information flow. This method is effective and feasible for both static systems and dynamical systems including continuous time models and discrete time series. The new method is applied to an example of linear dynamical models and the Lorenz system, and the results indicate that the method could find out the most influential variables, which conforms with entropy method, and it also give more detailed information for system analysis.