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研究生:鍾年勝
研究生(外文):CHUNG, NIEN-CHUNG
論文名稱:基於經驗模態分解與技術指標屬性篩選的混合型支援向量迴歸模型-應用於股票市場預測
論文名稱(外文):Hybrid SVR model based on EMD and technical indicators feature selection for stock forecasting
指導教授:魏良穎魏良穎引用關係
指導教授(外文):Liang-Ying Wei
口試委員:劉經緯林志賢魏良穎
口試委員(外文):LIU, JING-WEILIN, ZHI-XIANWEI, LIANG-YING
口試日期:2017-06-12
學位類別:碩士
校院名稱:元培醫事科技大學
系所名稱:資訊管理系數位創新管理碩士班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:31
中文關鍵詞:支援向量迴歸經驗模態分解股票預測
外文關鍵詞:support vector regression (SVR)empirical mode decomposition (EMD)TAIEX forecasting
相關次數:
  • 被引用被引用:1
  • 點閱點閱:125
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
摘 要
在時間序列的預測研究領域中,股票預測是一個重要且受到研究者感興趣的主題,能夠正確的預測股價在財務預測的過程中被認為是一件具有挑戰性的工作,然而在傳統的時間序列方法有三個主要的缺點: (1)某些時間序列的方法不能應用在所有的資料集當中,因為相關的模型必須符合其對應的統計假設;(2) 大部分時間序列使用的股票資料中包含內含的干擾,如此一來會減低預測的績效;(3)經由個人的經驗主觀的選擇技術指標,將導致較低的預測正確率,為了解決上述的問題,本研究提出了一個混合的時間序列支援向量迴歸模型,基於經驗模態分解與技術指標的屬性篩選的預測模型,用來預測台灣股票的大盤股價,為了驗證本研究所提出的方法,本文採用九年期的TAIEX (TAIWAN STOCK EXCHANGE CAPITALIZATION WEIGHTED STOCK INDEX)股票指數,從1997年到2005年,作為實驗數據,並以均方根誤差(RMSE)為評價標準, 實驗結果顯示所提出的模型在均方根誤差的評估標準之下優於實驗中所列出的方法。
關鍵字:支援向量迴歸、經驗模態分解、股票預測
Abstract
Stock forecasting is an important and interesting topic in time series forecasting. Accurate stock price is regard as a challenging task of the financial time forecasting process. However, there are three major drawbacks in stock market by traditional time-series model (1) some models can not be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance; (3) Subjective selected technical indicators as input variable by personal experience would lead to lower forecasting accuracy. For solving above problems, this paper proposes a hybrid time-series support vector regression (SVR) model based on Empirical mode decomposition (EMD) and technical indicators feature selection to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In verification, this paper employs nine year period of TAIEX stock index, from 1997 to 2005, as experimental datasets, and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.

Keywords:support vector regression (SVR), empirical mode decomposition (EMD), TAIEX forecasting.

口試委員審定書 I
誌 謝 II
中文摘要 III
英文摘要 IV
目 錄 V
圖 次 VI
表 次 VII
第一章 緒論 1
1.1 研究背景與動機 1
第二章 文獻探討 3
2.1 在股票市場上不同的預測模式 3
2.2 經驗模態分解法(EMPIRICAL MODE DECOMPOSITION) 3
2.3 逐步迴歸(STEPWISE REGRESSION) 5
2.4 技術指標(TECHNICAL INDICATORS) 5
2.5 時間序列預測法(TIME SERIES PREDICTION METHOD) 5
2.6 支援向量迴歸(SUPPORT VECTOR REGRESSION) 6
第三章提出的方法 7
3.1 研究方法與設計 7
第四章實驗驗證與比較 10
4.1 實際案例研究 10
4.2 模式驗證 13
第五章 討論與發現 16
5.1實驗結果的討論 16
5.2實驗結果的發現 21
第六章 結論與未來研究 23
6.1 結論 23
6.2 未來研究 23
參考文獻 25
附錄A 28
附錄B 29
參考文獻
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