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FORECASTING NIKKEI 225 INDEX WITH SUPPORT VECTOR MACHINE

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Journal of Systems Science & Complexity 2003年第4期16卷 415-423页

作者： HUANGWei YoshiteruNakamori wangshouyang YULean SchoolofKnowledgeScience JapanAdvancedInstituteofScienceandTechnology1-1AsahidaiTatstmokuchiIshikawa923-1292Japan InstituteofSystemsScience AcademyofMathematicsandSystemsSciencesChineseAcademyofSciencesB SchoolofKnowledgeScience JapanAdvancedInstituteofScienceandTechnology1-1AsahidaiTatsunokuchiIshikawa923-1292Japan InstituteofSystemsScience AcademyofMathematicsandSystemsSciencesChineseAcademyofSciencesBeijing100080China

Support Vector Machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare the perfor-mance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms other classification methods. Furthermore, we propose a combining model by integrating SVM with other classification methods. The combining model performs the best among the forecasting methods.

关键词： support vector machine forecasting multivariate classification

CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY

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Journal of Systems Science & Complexity 2005年第2期18卷 145-166页

作者： wangshouyang YULean K.K.LAI InstituteofSystemsScience AcademyofMathematicsandSystemsScienceChineseAcademyofSciencesBeijing100080China DepartmentofManagementSciences CityUniversityofHongKongTatCheeAvenueKowloonHongKong CollegeofBusinessAdministration HunanUniversityChangsha410082China

The difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting crude oil prices. However, all of the existing models of prediction can not meet practical needs. Very recently, Wang and Yu proposed a new methodology for handling complex systems-TEI@I methodology by means of a systematic integration of text mining, econometrics and intelligent *** the framework of TEI@I methodology, econometrical models are used to model the linear components of crude oil price time series (i.e., main trends) while nonlinear components of crude oil price time series (i.e., error terms) are modelled by using artificial neural network (ANN) models. In addition, the impact of irregular and infrequent future events on crude oil price is explored using web-based text mining (WTM) and rule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecasting approach with error correction and judgmental adjustment is formulated to improve prediction performance within the framework of the TEI@I methodology. The proposed methodology and the novel forecasting approach are illustrated via an example.

关键词： TEI@I methodology oil price forecasting text mining econometrics intelligence integration

A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING

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Journal of Systems Science & Complexity 2004年第3期17卷 297-305页

作者： HUANGWei NAKAMORIYoshiteru wangshouyang SchoolofKnowledgeScience JapanAdvancedInstituteofScienceandTechnologyAsahidai1-1TatsunokuchiIshikawa923-1292Japan InstituteofSystemsScience AcademyofMathematicsandSystemsScienceChineseAcademyofSciencesBeijing100080China

Input selection is probably one of the most critical decision issues in neural network designing, because it has a great impact on forecasting performance. Among the many applications of artificial neural networks to finance, time series forecasting is perhaps one of the most challenging issues. Considering the features of neural networks, we propose a general approach called Autocorrelation Criterion (AC) to determine the inputs variables for a neural network. The purpose is to seek optimal lag periods, which are more predictive and less correlated. AC is a data-driven approach in that there is no prior assumptiona bout the models for time series under study. So it has extensive applications and avoids a lengthy experimentation and tinkering in input selection. We apply the approach to the determination of input variables for foreign exchange rate forecasting and conductcomparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms information-based in-sample model selection criterion.

关键词： input variables foreign exchange rate neural networks time seriesforecasting

AN EMPIRICAL ANALYSIS OF SAMPLING INTERVAL FOR EXCHANGE RATE FORECASTING WITH NEURAL NETWORKS

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Journal of Systems Science & Complexity 2003年第2期16卷 165-176页

作者： K.K.Lai Y.Nakamori wangshouyang DepartmentofManagementSciences CityUniversityofHongKongTatCheeAvenueKowloonHongKong SchoolofKnowledgeScience JapanAdvancedInstituteofScienceandTechnology1-1AsahidaiTatsunokuchiIshikawa923-1292Japan InstituteofSystemsScience AcademyofMathematicsandSystemsSciencesChineseAcademyofSciencesBeijing100080China

Artificial neural networks (ANNs) have been widely used as a promising alternative approach for forecast task because of their several distinguishing features. In this paper, we investigate the effect of different sampling intervals on predictive performance of ANNs in forecasting exchange rate time series. It is shown that selection of an appropriate sampling interval would permit the neural network to model adequately the financial time series. Too short or too long a sampling interval does not provide good forecasting accuracy. In addition, we discuss the effect of forecasting horizons and input nodes on the prediction performance of neural networks.

关键词： Neural networks sampling interval exchange rate forecasting.

TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS:SELECTING OR COMBINING?

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Journal of Systems Science & Complexity 2005年第1期18卷 1-18页

作者： YULean wangshouyang K.K.Lai Y.Nakamori InstituteofSystemsScience AcademyofMathematicsandSystemsScienceChineseAcademyofSciencesBeijing100080China//SchoolofManagementGraduateSchoolofChineseAcademyofSciencesBeijing100049China InstituteofSystemsScience AcademyofMathematicsandSystemsScienceChineseAcademyofSciencesBeijing100080China//InstituteofPolicyandPlanningSciencesUniversityofTsukubaTsukubaIbaraki305-8573Japan DepartmentofManagementSciences CityUniversityofHongKongTatCheeAvenueKowloonHongKong SchoolofKnowledgeScience JapanAdvancedInstituteofScienceandTechnology1-1AsahidaiIshikawa923-1292Japan

Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this *** results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.

关键词： time series forecasting model selection stability robustness combiningforecasts