Information Geometric Theory in the Prediction of Abrupt Changes in System Dynamics |
| |
Authors: | Adrian-Josue Guel-Cortez Eun-jin Kim |
| |
Institution: | Centre for Fluid and Complex Systems, Coventry University, Priory St, Coventry CV1 5FB, UK; |
| |
Abstract: | Detection and measurement of abrupt changes in a process can provide us with important tools for decision making in systems management. In particular, it can be utilised to predict the onset of a sudden event such as a rare, extreme event which causes the abrupt dynamical change in the system. Here, we investigate the prediction capability of information theory by focusing on how sensitive information-geometric theory (information length diagnostics) and entropy-based information theoretical method (information flow) are to abrupt changes. To this end, we utilise a non-autonomous Kramer equation by including a sudden perturbation to the system to mimic the onset of a sudden event and calculate time-dependent probability density functions (PDFs) and various statistical quantities with the help of numerical simulations. We show that information length diagnostics predict the onset of a sudden event better than the information flow. Furthermore, it is explicitly shown that the information flow like any other entropy-based measures has limitations in measuring perturbations which do not affect entropy. |
| |
Keywords: | information geometry information length information flow abrupt events prediction entropy |
|
|