The K.U. Leuven competition data a challenge for advanced neural network techniques.pdf

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The K.U. Leuven competition data a challenge for advanced neural network techniques.pdf

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The K.U. Leuven competition data a challenge for advanced neural network techniques

The K.U. Leuven CompetitionData: a Challenge for AdvancedNeural Network TechniquesJ.A.K. Suykens and J. VandewalleKatholieke Universiteit Leuven, Dept. Electr. Eng. ESAT/SISTAKardinaal Mercierlaan 94, B-3001 Heverlee, BelgiumEmail: johan.suykens@esat.kuleuven.ac.beAbstract. In this paper we shortly discuss the K.U. Leuven time-seriesprediction competition, which has been held in the framework of the In-ternational Workshop on Advanced Black-Box Techniques for NonlinearModeling, K.U.Leuven Belgium July 8-10 1998. The data are related toa 5-scroll attractor, generated from a generalized Chuas circuit. Thetime-series consists of a given set of 2000 data points, where the next200 points are to be predicted. In total, 17 entries have been submitted.The winning contribution by McNames succeeds in making an accurateprediction over a time horizon of about 300 points using a nearest tra-jectory method, which incorporates local modeling and cross-validationtechniques. The competition data can serve as a challenging test-casefor advanced nonlinear modelling techniques, including neural networks.The data are able to reveal shortcomings of many methods.Keywords. Time-series prediction, generalized Chuas circuit, chaos.1. IntroductionThe prediction of time-series is a challenging area for nonlinear modeling tech-niques. This has been demonstrated earlier by the Santa Fe time series pre-diction competition [17], where several data sets such as laser data, currencyexchange rates and astrophysical data were to be predicted. Despite the factthat chaotic systems have a limited predictability, related to the rst Lyapunovexponent of the system, it has been shown that accurate short term forecastsare feasible. In this context the choice of the model structure and embedding,This research work was carried out at the ESAT laboratory and the InterdisciplinaryCenter of Neural Networks ICNN of the Katholieke Universiteit Leuven, in the framework ofthe FWO project G.0262.97 Learning and Optimi