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研究生:駱瑜婕
研究生(外文):Luo, Yu-Chieh
論文名稱:應用智慧型混合方法以預測臺灣股價指數期貨
論文名稱(外文):Applying Intelligent Hybrid Approaches to the Prediction of TAIEX Futures
指導教授:邵曰仁邵曰仁引用關係
指導教授(外文):Yehjen E. Shao
口試委員:侯家鼎呂奇傑
口試委員(外文):Chia Ding HouChi Jie Lu
口試日期:2015-06-28
學位類別:碩士
校院名稱:輔仁大學
系所名稱:統計資訊學系應用統計碩士在職專班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:60
中文關鍵詞:類神經網路
外文關鍵詞:Artificial Neural Network
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由於期貨合約往往會影響一國其收入水平、物價指數和其他許多重要的宏觀經濟變量,因此,近年來預測期貨合約引起廣泛的注意。相較於應用典型的單一階段預測模型,本文提出了混合方法以預測臺灣證券交易所發行量加權股價指數(Taiwan Stock Exchange Capitalization Weighted Stock Index; TAIEX)。本文提出多元迴歸(Multiple Regression; MR)、類神經網路(Artificial Neural Network;
ANN)、支援向量迴歸(Support Vector Regression; SVR)及多元適應性雲形迴歸(Multivariate Adaptive Regression Splines; MARS)組合而成的混合模型,本文以多元迴歸及多元適應性雲形迴歸方法進行篩選較少但重要的解釋變數,本文進而使用這些重要的解釋變數,應用類神經網路及支援向量迴歸機技術進行TAIEX預測。本文蒐集並運用2005年1月至2014年12月的真實數據,進行模型建置及驗證,研究結果顯示,
本文所提出混合方法有優異的預測績效表現。
Because the futures contract (FC) by a country often affects its income levels, price index and many other important macroeconomic variables, the prediction of FC has attracted considerable attention in recent years. In contrast to the typical single stage forecast model, this study proposes a hybrid forecasting approach to Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), Support Vector Regression (SVR) and multivariate adaptive regression splines (MARS) components. The MR and/or MARS component of the hybrid models are established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN and/or SVR component are then designed to generate forecasts based on those important explanatory variables. Subsequently, this study the model is used to analyze a real dataset from January, 2005 to December, 2014. The prediction results reveal that the proposed hybrid approaches exhibits superior forecasting performance for predicting the TAIEX
目 錄
第壹章 緒論……………..………………………………………………1
第一節 研究背景……………………………..……………………1
第二節 研究動機與目的…………………………..………………2
第三節 研究流程與架構………………………..…………………3
第貳章 文獻探討……………………………………..…………………5
第一節 技術指標分析之應用……………..………………………6
第二節 時間序列預測之應用……………………………………..6
第三節 多元迴歸之運用…………………………………..………7
第四節 類神經網路之預測………………………………………..7
第五節 支援向量迴歸之應用……………………………………..8
第六節 多元適應性雲形迴歸……………………………………..8
第參章 研究方法………………………………………………………10
第一節 解釋名詞…………………………………………………10
第二節 時間序列…………….…………………………………...12
第三節 多元迴歸……………………………………..…………..15
第四節 類神經網路………………………………………..……..16
第五節 支援向量迴歸……………………………………...…….18
第六節 多元適應性雲形迴歸……………………………………19
第四章 實證研究………………………………………………………21
第一節 我國臺股期貨指數簡介…………………….……….…..21
第二節 單一階段預測分析…………….………………………...21
第三節 混合階段預測分析…………………………………..…..37
第四節 結果比較………………………………………….….…..55
第五章 結論…………………………………………………...….……59
第一節 研究發現………………………………………..………..59
第二節 未來研究方向…………….………………………….…..60
參考文獻…………………………………………………………….….61




















表 目 錄
表4-2-1條件式最小平方估計(原始參數估計)……………………....23
表4-2-2條件式最小平方估計(移除常數項之參數估計)…………..24
表4-2-3條件式最小平方估計(符合檢定之參數估計)…………....24
表4-2-4殘差的自相關檢查……………………………………………25
表4-2-5 ARIMA預測能力指標…………………...…………………...26
表4-2-6原始資料分析…………………………………………………27
表4-2-7共線性統計量分析結果………………………………………27
表4-2-8顯著性檢定結果………………………………………………28
表4-2-9迴歸分析預測能力指標…………………………………..…..29
表4-2-10 ANN分析預測能力指標…………………………...………..35
表4-2-11參數設定……………………………………………………...36
表4-2-12 SVR預測能力指標………………………………...………...36
表4-2-13 MARS篩選變數分析……………………………...………...37
表4-2-14 MARS預測能力指標………………………………...……...38
表4-3-1 MR-ANN預測能力指標……………………..………….……44
表4-3-2 MR-SVR預測能力指標……………………...…………….....45
表4-3-3 MARS篩選變數分析………………………...……………….46
表4-3-4 MR-MARS預測能力指標…………………………………….46
表4-3-5原始資料分析…………………………………………………47
表4-3-6共線性統計量分析結果……………………………………....47
表4-3-7逐步迴歸分析結果………………………………………...….48
表4-3-8 MARS-MR預測能力指標………….….………………….….48
表4-3-9 MARS-ANN預測能力指標……………...…………………...55
表4-3-10 MARS-SVR預測能力指標…………………...……………..56
表4-4-1單一階段預測能力指標比較………………………………....57
表4-4-2混合階段預測能力指標比較………………………………....57
表4-4-3 MR-ANN預測模型改善率……………………..…………....58
表4-4-4 MARS-ANN預測模型改善率……………...………………...59


















圖 目 錄
圖1-3-1研究流程圖…………………………………………….……….5
圖3-5-1倒傳遞類神經網路……………………………………………17
圖4-1-1原始資料實際值………………………………………..……..22
圖4-2-1 ARIMA自相關(ACF)與偏自相關(PACF)……………..23
圖4-2-2 ARIMA預測值……………………………...…………….…..25
圖4-2-3迴歸分析預測值…………………………………………...….28
圖4-2-4 ANN預測值(20, 0.01)……………………...……………..29
圖4-2-5 ANN預測值(21, 0.01)……………………...……………..30
圖4-2-6 ANN預測值(22, 0.01)……………………...……………..30
圖4-2-7 ANN預測值(23, 0.01)…..…………………..…………….31
圖4-2-8 ANN預測值(24, 0.01)………………………..…………...31
圖4-2-9 ANN預測值(20, 0.001)……………………….…………..32
圖4-2-10 ANN預測值(21, 0.001)………………………..………….32
圖4-2-11 ANN預測值(22, 0.001)…………………………………...33
圖4-2-12 ANN預測值(23, 0.001)….…………………….………….33
圖4-2-13 ANN預測值(24, 0.001)…………………………………...34
圖4-2-14 SVR預測值……………………………………......…….…..36
圖4-2-15 MARS預測值……………………………………...………...38
圖4-3-1 MR-ANN預測值(4, 0.01)………………………..…………..39
圖4-3-2 MR-ANN預測值(5, 0.01)……………………..…………..39
圖4-3-3 MR-ANN預測值(6, 0.01)…………………….…………..40
圖4-3-4 MR-ANN預測值(7, 0.01)…..………………….…………40
圖4-3-5 MR-ANN預測值(8, 0.01)……………………….………..41
圖4-3-6 MR-ANN預測值(4, 0.001)………………………………..41
圖4-3-7 MR-ANN預測值(5, 0.001)….. ……………………………42
圖4-3-8 MR-ANN預測值(6, 0.001)………………………....……..42
圖4-3-9 MR-ANN預測值(7, 0.001)…. ……………………..…….43
圖4-3-10 MR-ANN預測值(8, 0.001)……………………………..…..43
圖4-3-11 MR-SVR預測值…………………………………………......45
圖4-3-12 MR-MARS預測值…………………………………………...46
圖4-3-13 MARS-MR預測值…. ……………………….………………48
圖4-3-14 MARS-ANN預測值(4, 0.01)……………………………….49
圖4-3-15 MARS-ANN預測值(5, 0.01)……………………………….50
圖4-3-16 MARS-ANN預測值(6, 0.01)…………….…………………50
圖4-3-17 MARS-ANN預測值(7, 0.01)……………………………….51
圖4-3-18 MARS-ANN預測值(8, 0.01)………………...……………..51
圖4-3-19 MARS-ANN預測值(4, 0.001)……………….……………..52
圖4-3-20 MARS-ANN預測值(5, 0.001)……………….……………..52
圖4-3-21 MARS-ANN預測值(6, 0.001)…. ………………………….53
圖4-3-22 MARS-ANN預測值(7, 0.001)……………….……………..53
圖4-3-23 MARS-ANN預測值(8, 0.001)……………….……………..54
圖4-3-24 MARS-SVR預測值……………………………..…………...56

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