Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs

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Author(s): Lean Yu (City University of Hong Kong, Hong Kong), Shouyang Wang (Chinese Academy of Sciences, China)and Kin Keung Lai (Chinese Academy of Sciences, China)
Copyright: 2009
Pages: 14
Source title: Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery
Source Author(s)/Editor(s): Hsiao-Fan Wang (National Tsing Hua University, ROC)
DOI: 10.4018/978-1-59904-982-3.ch017

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Financial crisis is a kind of typical rare event, but it is harmful to economic sustainable development if occurs. In this chapter, a Hilbert-EMD-based intelligent learning approach is proposed to predict financial crisis events for early-warning purpose. In this approach a typical financial indicator currency exchange rate reflecting economic fluctuation condition is first chosen. Then the Hilbert-EMD algorithm is applied to the economic indicator series. With the aid of the Hilbert-EMD procedure, some intrinsic mode components (IMCs) of the data series with different scales can be obtained. Using these IMCs, a support vector machine (SVM) classification paradigm is used to predict the future financial crisis events based upon some historical data. For illustration purposes, two typical Asian countries including South Korea and Thailand suffered from the 1997-1998 disastrous financial crisis experience are selected to verify the effectiveness of the proposed Hilbert-EMD-based SVM methodology.

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Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs

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