dc.contributor.advisor | 吳柏林 | zh_TW |
dc.contributor.advisor | Wu, Berlin | en_US |
dc.contributor.author (Authors) | 許毓云 | zh_TW |
dc.contributor.author (Authors) | Hsu, Yi-Yun | en_US |
dc.creator (作者) | 許毓云 | zh_TW |
dc.creator (作者) | Hsu, Yi-Yun | en_US |
dc.date (日期) | 1998 | en_US |
dc.date.accessioned | 18-Sep-2009 18:28:04 (UTC+8) | - |
dc.date.available | 18-Sep-2009 18:28:04 (UTC+8) | - |
dc.date.issued (上傳時間) | 18-Sep-2009 18:28:04 (UTC+8) | - |
dc.identifier (Other Identifiers) | B2002001687 | en_US |
dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/36392 | - |
dc.description (描述) | 碩士 | zh_TW |
dc.description (描述) | 國立政治大學 | zh_TW |
dc.description (描述) | 應用數學研究所 | zh_TW |
dc.description (描述) | 86751005 | zh_TW |
dc.description (描述) | 87 | zh_TW |
dc.description.abstract (摘要) | Abstract In time series analysis, we often find the trend of dynamic data changing with time. Using the traditional model fitting can`t get a good explanation for dynamic data. Therefore, many scholars developed various methods for model construction. The major drawback with most of the methods is that personal viewpoint and experience in model selection are usually influenced in them. Therefore, this paper presents a new approach on genetic-based modeling for the nonlinear time series. The research is based on the concepts of evolution theory as well as natural selection. In order to find a leading model from the nonlinear time series, we make use of the evolution rule: survival of the fittest. Through the process of genetic evolution, the AIC (Akaike information criteria) is used as the adjust function, and the membership function of the best-fitted models are calculated as performance index of chromosome. Empirical example shows that the genetic model can give an efficient explanation in analyzing Taiwan exchange rates, especially when the structure change occurs. | en_US |
dc.description.tableofcontents | Contents ABSTRACT LIST OF TABLE LIST OF FIGURE 1. Introduction……………………………………………………………………1 2. Genetic Modeling………………………………………………………………4 3. Procedure of Genetic Modeling…………………………………………………9 4. Combined Forecasting with Gene Models……………………………………11 5. Simulation………………………………………………………………………13 6. An Empirical Application for Exchange Rates………………………………18 7. Conclusion………………………………………………………………………21 References…………………………………………………………………………22 | zh_TW |
dc.language.iso | en_US | - |
dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#B2002001687 | en_US |
dc.subject (關鍵詞) | 非線性時間數列 | zh_TW |
dc.subject (關鍵詞) | 遺傳建模 | zh_TW |
dc.subject (關鍵詞) | 主導模式 | zh_TW |
dc.subject (關鍵詞) | 隸屬度函數 | zh_TW |
dc.subject (關鍵詞) | 匯率 | zh_TW |
dc.subject (關鍵詞) | Nonlinear time series | en_US |
dc.subject (關鍵詞) | Genetic modeling | en_US |
dc.subject (關鍵詞) | Leading models | en_US |
dc.subject (關鍵詞) | Membership function | en_US |
dc.subject (關鍵詞) | Exchange rates | en_US |
dc.title (題名) | 遺傳模式在匯率上分析與預測之應用 | zh_TW |
dc.title (題名) | Genetic Models and Its Application in Exchange Rates Analysis and Forecasting | en_US |
dc.type (資料類型) | thesis | en |
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