Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, An-Pinen_US
dc.contributor.authorHsu, Yu-Chiaen_US
dc.contributor.authorHu, Ko-Feien_US
dc.date.accessioned2014-12-08T15:01:57Z-
dc.date.available2014-12-08T15:01:57Z-
dc.date.issued2008en_US
dc.identifier.isbn978-0-7695-3304-9en_US
dc.identifier.urihttp://hdl.handle.net/11536/686-
dc.identifier.urihttp://dx.doi.org/10.1109/ICNC.2008.298en_US
dc.description.abstractIn this work, a multi-neural network model consisting of three sub-networks and one master network is proposed to combine the fundamental theorem and technical analysis in TWD/USD exchange rate forecasting. The long-term, mid-term, and short-term tendencies of exchange rate are forecasted separately by different sub-networks. Five macro economics factors of price level, interest rates, money supply, imports/exports, and productivity, and seven practical technical indicators of fifteen-day and one-day intervals are selected as the input variables of the three sub-networks. The master network then provides the integrated forecasting according to the three sub-networks. To increase forecasting accuracy, a threshold filtering mechanism was applied in this work. The experiment result shows that the multi-neural network is more effective than the random walk model and single-neural network model, and with the threshold filtering can achieve high accuracy.en_US
dc.language.isoen_USen_US
dc.titleA Hybrid Forecasting Model for Foreign Exchange Rate Based on a Multi-neural Networken_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/ICNC.2008.298en_US
dc.identifier.journalICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 5, PROCEEDINGSen_US
dc.citation.spage293en_US
dc.citation.epage298en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000264556100060-
Appears in Collections:Conferences Paper


Files in This Item:

  1. 000264556100060.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.