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政大典藏 > College of Commerce > Department of MIS > Periodical Articles > Item 140.119/98787

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/98787

Title:	An Integraged Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting
Authors:	楊亨利 Yang, Heng-Li;Lin, Han-Chou
Contributors:	資管系
Keywords:	Financial time series forecasting;empirical mode decomposition;intrinsic mode function;ARIMA;support vector regression
Date:	2016-04
Issue Date:	2016-07-07 17:03:35 (UTC+8)
Abstract:	Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.
Relation:	International Journal on Artificial Intelligence Tools, 25(2), 1650005
Data Type:	article
DOI link:	http://dx.doi.org/10.1142/S0218213016500056
DOI:	10.1142/S0218213016500056
Appears in Collections:	[Department of MIS] Periodical Articles

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