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		<citationkey>CarliDuarMota:2010:ImFoCa</citationkey>
		<title>Improving the forecasting capabilities in time series analysis</title>
		<format>On-line.</format>
		<year>2010</year>
		<secondarytype>PRE CI</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>232 KiB</size>
		<author>Carli, Henrique,</author>
		<author>Duarte, Luiz Guilherme,</author>
		<author>Mota, Luis Antônio C. P,</author>
		<editor>Macau, Elbert Einstein Nehrer,</editor>
		<editor>Turci, Luiz Felipe Ramos,</editor>
		<editor>Martins Filho, Luiz Siqueira,</editor>
		<e-mailaddress>felipeturci@yahoo.com.br</e-mailaddress>
		<conferencename>Dynamics Days South America 2010 - International conference on chaos and nonlinear dynamics.</conferencename>
		<conferencelocation>São José dos Campos</conferencelocation>
		<date>July 26-30, 2010</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>Applications of nonlinear sciences, chaotic dynamics, time series analysis.</keywords>
		<abstract>For any observed system, physical or otherwise, one generally wishes to make predictions on its future evolution. Sometimes, very little is known about the system. If a time series is the only source of information on the system, prediction of the future values of the series requires a modelling of the system's (perhaps nonlinear) dynamical law. In particular, one is interested on the forecasting capabilities of the global approach to time series analysis. This can be a very complex and computationaly expensive procedure. So, there is a clear demand for procedures that can, without increasing the degree of the global mapping, enhance the accuracy of such mappings.</abstract>
		<area>COMP</area>
		<language>en</language>
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