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- Kamruzzaman Joarder
- Faculty of Information Technology, Monash University
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- Sarker Ruhul A
- School of IT & EE, University of NSW, ADFA Campus
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Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 124 (10), 1944-1951, 2004
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390001204606632832
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- NII論文ID
- 10013640669
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 7105322
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可