An introduction to the NMPCGraph as general schema for causal modelling of nonlinear, multivariate, dynamic, and recursive systems with focus on timeseries prediction
Abstract
While the disciplines of physics and engineering sciences in many cases have taken advantage from accurate timeseries prediction of system behaviour by applying ordinary differential equation systems upon precise basic physical laws such approach hardly could be adopted by other scientific disciplines where precise mathematical basic laws are unknown. A new modelling schema, the NMPCgraph, opens the possibility of interdisciplinary and generic nonlinear, multivariate, dynamic, and recursive causal modelling in domains where basic laws are only known as qualitative relationships among parameters while their precise mathematical nature remains undisclosed at modelling time. The symbolism of NMPCgraph is kept simple and suited for analysts without advanced mathematical skills. This article presents the definition of the NMPCgraph modelling method and its six component types. Further, it shows how to solve the inverse problem of deriving a nonlinear ordinary differential equation system from any NMPCgraph in conjunction with historic calibration data by means of machine learning. This article further discusses how such a derived NMPCmodel can be used for hypothesis testing and timeseries prediction with the expectation of gaining prediction accuracy in comparison to conventional prediction methods.
 Publication:

arXiv eprints
 Pub Date:
 November 2015
 arXiv:
 arXiv:1511.00319
 Bibcode:
 2015arXiv151100319J
 Keywords:

 Computer Science  Systems and Control;
 9302;
 G.1.2;
 I.2.6;
 I.2.8
 EPrint:
 29 pages, containing glossary