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