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Modeling dynamical systems with data stream mining

We address the task of modeling dynamical systems in discrete time using 
 regression trees, model trees and option trees for on-line regression. Some 
 challenges that modeling dynamical systems pose to data mining approaches are 
 described: these motivate the use of methods for mining data streams. The 
 algorithm FIMT-DD for mining data streams with regression or model trees is 
 described, as well as the FIMT-DD based algorithm ORTO, which learns option 
 trees for regression. These methods are then compared on several case 
 studies, i.e., tasks of learning models of dynamical systems from observed 
 data. The experimental setup, including the datasets, and the experimental 
 results are presented in detail. These demonstrate that option trees for 
 regression work best among the considered approaches for learning models of 
 dynamical systems from streaming data.