Optimize stock price variation prediction via DOE and BPNN

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Please use this identifier to cite or link to this item: http://chur.chu.edu.tw/handle/987654321/29196

Title:	Optimize stock price variation prediction via DOE and BPNN
Authors:	謝玲芬 Hsieh, Ling-Feng
Contributors:	運輸科技與物流管理學系 Transportation Technology and Logistics Management
Keywords:	Stock price forecasting;Back-propagation neural network;Design of experiment;Parameter optimization t
Date:	2010
Issue Date:	2014-06-27 00:21:38 (UTC+8)
Abstract:	Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always de
Appears in Collections:	[Department of Transportation Technology and Logistice Management] Seminar Papers

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