Empirical comparison of vector bootstrap methods for multivariate scenario generation

dc.contributor.author	Murray, Malcolm James
dc.date.accessioned	2012-07-19T09:37:57Z
dc.date.available	2012-07-19T09:37:57Z
dc.date.issued	2012-07-19
dc.description.abstract	Stochastic simulation models require input scenarios, which may be generated from observed data using bootstrap methods. If a model’s input variables are auto- and/or cross-correlated, these dependencies must be preserved in the generated scenarios. Three bootstrap methods were tested empirically: (1) the vector moving block bootstrap method, with a block length of one timeframe, (2) the vector moving block bootstrap method, with an optimized block length, and (3) the vector nearest neighbour bootstrap method. They were applied to data observed from processes at a petro-chemical plant: 28 numerical, multivariate, stationary time series, with a variety of auto- and cross-correlations. The quality of the generated scenarios was measured using a Turing test procedure, which balances fidelity to the observed data and natural variety. Method (2) performed best, followed by method (3), and then method (1). The number of input variables bootstrapped simultaneously did not significantly affect the performance of the bootstrap methods.	en_ZA
dc.identifier.uri	http://hdl.handle.net/10539/11711
dc.language.iso	en	en_ZA
dc.title	Empirical comparison of vector bootstrap methods for multivariate scenario generation	en_ZA
dc.type	Thesis	en_ZA

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