
Digital Library of the
European Council for Modelling and Simulation 
Title: 
Scenario
Tree Generation By Clustering The Simulated Data Paths 
Authors: 
Henrikas Pranevicius, Kristina Sutiene 
Published in: 
ECMS
2007 Proceedings Edited
by: Ivan Zelinka, Zuzana Oplatkova, Alessandra Orsoni ISBN:
9780955301827 Doi: 10.7148/2007 21^{st} European
Conference on Modelling and Simulation, Prague, June
46, 2007 
Citation
format: 
Pranevicius, H., & Sutiene,
K. (2007). Scenario Tree Generation By Clustering The Simulated Data Paths.
ECMS 2007 Proceedings edited by: I. Zelinka, Z. Oplatkova, A. Orsoni
(pp. 203208). European Council for Modeling and Simulation. doi:10.7148/20070203. 
DOI: 
http://dx.doi.org/10.7148/20070203 
Abstract: 
Multistage stochastic programs are
effective for solving longterm planning problems under uncertainty. Such
programs are usually based on a scenario model of future environment developments. A good
approximation of the underlying stochastic process may involve a very large
number of scenarios and their probabilities. We discuss the case when enough
data paths can be generated, but due to solvability of stochastic program the
scenario tree has to be constructed. The proposed strategy is to generate the
multistage scenario tree from the set of individual scenarios by bundling
scenarios based on cluster analysis. The Kmeans clustering approach is
modified to capture the interstage dependencies in
order to model the sequential decisions. The described scenario tree
generation method is implemented on sampled data of nominal interest rate. 
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