跳到主要內容

臺灣博碩士論文加值系統

(54.224.133.198) 您好!臺灣時間:2022/01/29 22:12
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:許育嘉
研究生(外文):Yu-Chia Hsu
論文名稱:結合小波分解和小波神經網路於非定性財務時間序列之預測
論文名稱(外文):Combining Wavelet Decomposition and Wavelet Neural Network for Non-Stationary Financial Time Series Forecasting
指導教授:陳安斌陳安斌引用關係
指導教授(外文):An-Pin Chen
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊管理所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:60
中文關鍵詞:時間序列小波轉換神經網路股市預測非定性
外文關鍵詞:time serieswavelet transformneural networkstock market forecastingnon-stationary
相關次數:
  • 被引用被引用:11
  • 點閱點閱:608
  • 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:6
傳統時間序列的分析,通常都植基於機率與統計學,並假設資料的性質是定性(stationary)和線性(linear)的。但是當系統動態呈現高度非線性並伴隨著非定性(non-stationary)時,這些傳統模型的適用性及準確性可能無法滿足研究的需求。因此本研究提出了『小波神經網路多尺度解析混合預測模型』,可適用於非定性時間序列之分析與預測。利用小波分解具有處理混沌訊號的能力,將原始時間序列分解成多個解析尺度之子序列,再結合具有廣域函數逼近能力之小波神經網路架構,建構出時間序列混合預測模型。期望能在處理非定性時間序列分析時,不受限於傳統模型之假設條件,且能準確地預測。研究中並以台灣證券交易所發行量加權股價指數為實例,應用於一日及五日之收盤價與價格變化率預測,且與傳統自回歸模型以及未經小波分解步驟之小波神經網路模型之預測結果做比較。研究的結果顯示,『小波神經網路多尺度解析混合預測模型』可準確地預測非定性時間序列,其預測結果較前兩者比較模型準確,並具有參考價值可提供制訂決策之參考。
Traditional time series analysis methodologies are based on probability and statistics with the assumption of stationary and linear properties. However, the system dynamic of time series usually arise with highly nonlinear and non-stationary properties, these conventional time series forecasting models cannot satisfy the feasibility and accuracy of which research desires. Consequently, the “multi-resolution wavelet neural network hybrid forecasting model,” capable of adaptive forecasting non-stationary time series, is proposed in this research. The original time series is decomposed into subsequences in different resolution scale using the wavelet decomposition, which is efficient in processing chaotic signals. Furthermore, combined with the wavelet neural network architecture, which is referred to an universal function approximator, to establish the time series forecasting model, and expect this model to predict accurately and conquer the restriction of the traditional models when encounter non-stationary time series. The TAIEX of Taiwan stock market index is used for one and five day ahead forecasting of close price and price change to demonstrate the proposed model. Two other forecast results, one is obtained from traditional autoregressive model, and the other is without using the wavelet decomposition, were also used to compare with the proposed model. The experimental results indicate that the multi-resolution wavelet neural network hybrid forecasting model can accurately predict non-stationary financial time series and provide a valuable reference for making investing decision.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒 論 1
1.1 動機與背景 1
1.2 問題與目的 2
1.3 研究流程 6
1.4 論文架構 7
第二章 文獻探討 9
2.1 小波理論概述 9
2.1.1 小波轉換 9
2.1.2 多尺度解析分析(multi-resolution analysis,MRA) 12
2.1.3 時間序列之特徵擷取 14
2.2 小波神經網路 18
2.2.1 小波神經網路之簡介 18
2.2.2 小波神經網路的架構與流程 19
第三章 預測模型之建構與實驗設計 23
3.1 預測模型之建構 23
3.2 實驗設計與方法 28
第四章 實驗成果與探討 32
4.1 小波分解後之時間序列性質 32
4.1.1 平穩性質(stationary)的檢定與分解階層數的選擇 32
4.1.2 小波分解與移動平均線之關聯性 39
4.2預測模型之參數決定 42
4.2.1 小波分解尺度階層(scale level)的決定 42
4.2.2 預測模式階層(order)的決定 43
4.3 預測結果 48
第五章 結論與建議 54
5.1 結論 54
5.2 建議 55
參考文獻 56
表目錄
表 4.1 股價收盤價之ADF單根檢定結果 37
表 4.2 股價變化率(一日相對變化率)之ADF單根檢定結果 38
表 4.3 殘差與移動平均之敘述統計量(股價收盤價) 40
表 4.4 殘差與移動平均之敘述統計量(股價變化率) 40
表 4.3 預測模型階層選擇之Akaike’s FPE值-股價指數每日收盤價 45
表 4.4 預測模型階層選擇之Akaike’s FPEC值-股價指數每日變化率 46
表 4.5 預測模型階層數(輸入變數個數)選擇結果 48
表 4.6 各方法之預測結果比較 50
圖目錄
圖 1.1 研究流程 8
圖 2.1 小波轉換之lattice 11
圖 2.2 小波分解樹 13
圖 2.3 用於時間序列特徵擷取之 time-based à trous wavelet transform分解 19
圖 2.4 用於逼近(approximation)的小波神經網路架構 19
圖 2.5 小波神經網路中之母波(mother wavelet) 20
圖 3.1小波神經網路多尺度解析混合預測模式示意圖 23
圖 3.2小波神經網路多尺度解析混合預測模式流程圖 27
圖 3.3 本研究所採用之時間序列原始資料(1998/5/4~2002/2/27) 29
圖 4.1 小波分解前後之數列(1998/05/04~2002/02/27之股價收盤價) 33
圖 4.2 小波分解前後之數列(2001/04/05~2002/02/27之股價變化率) 34
圖 4.3股價收盤價之殘差( )與移動平均(MA_256)趨勢圖 41
圖 4.4股價變化率之殘差( )與移動平均(MA_256)趨勢圖 41
圖 4.5 AIC FPEC值與模型階層數之關係-股價收盤價 47
圖 4.6 FPEC值與模型階層數之關係-股價變化率 47
圖 4.7 『小波類神經網路多尺度解析混合預測模型』往前一日股價收盤價之各尺度階層預測結果 49
圖 4.8 各預測模型預測值與實際值比較-往前一日股價預測 52
圖 4.9 各預測模型預測值與實際值比較-往前五日股價預測 52
圖 4.10 各預測模型預測值與實際值比較-往前一日股價變化率 預測 53
圖 4.11 各預測模型預測值與實際值比較-往前五日股價變化率 預測 53
Assume, Alex, Campbell, Jonathan and Murtagh, Fionn (1998), Wavelet-based feature extraction and decomposition strategies for financial forecasting, Journal of computational intelligence in finance, March/April 1998.
Baba, Norio and Kozaki, Motokazu (1992), An Intelligent Forecasting System of Stock Price Using Neural Networks, IJCNN-1992 (1).
Bjorn, V. (1995), Multiresolution methods for financial time series prediction, Computational Intelligence for Financial Engineering,Proceedings of the IEEE/IAFE 1995, pp.97.
Cao, Liangyue, Hong, Yiguang, Fang, Haiping, and He, Guowei (1995), Predicting chaotic time series with wavelet networks, Physica D, 85(1-2), pp.225-238.
Cao, Liangyue, Hong, Yiguang, Zhao, Hanzhang and Deng, Shuhui (1996), Predicting economic time series using a nonlinear deterministic technique, Computational economics (9), pp.149-178.
Ciuca, I. ,and Ware, J. A. (1997), Wavelet networks as an alternative to neural networks, The 6th International Conference on Emerging Technologies and Factory Automation Proceedings, pp.353 —358.
Cristea, Paul, Tuduce, Rodica and Cristea, Alexandra (2000), Time Series Prediction with Wavelet Neural Networks, 5th Seminar on Neural Network Applications in Electrical Engineering, pp.5-10.
Delurgio, Stephen A. (1997), Forecasting, principles, and application, McGraw-Hill.
Geva, Amir B. (1998), ScaleNet-multiscale neural-network architecture for time series prediction, IEEE transactions on neural networks, 9(5).
Gonghui, Zheng, Starck, Jean-Luc, Campbell, Jonathan and Murtagh, Fionn (1999), The wavelet transform for filtering financial data streams, Journal of computational intelligence in finance, May/June 1999.
Jang, Gia-Shuh, Lai, Feipei , Jiang, Bor-Wei, and Chien, Li-Hua (1991), An Intelligent Trend Prediction and Reversal Recognition System Using Dual-module Neural Networks, The first International Conference on Artificial Intelligent Applications on Wall Street.
Juang, Jer-Nan (1994), Applied system identification, PTR Prentice Hall, Englewood Cliffs, N.J.
Kishikawa, Yoshinori and Tokinaga, Shozo (2000), Prediction of stock trends by using the wavelet transform and the multi-stage fuzzy inference system optimized by the GA, IEICE Trans. Fundamentals, E83-A(2).
Kosaka, M., Mizuno, H., Sasaki, T., Someya, R., Hamada,N. (1991), Applications of Fuzzy logic/Neural network to Securities Trading Decision Support System, Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics. (3), pp.1913-1918.
Liu, G. P., Billings, S. A., and Kadirkamanathan, V. (1998), Nonlinear system identification using wavelet networks, UKACC international Conference on Control ’98, IEE.
Moshiri, Saeed, Cameron, Norman E. and Scuse, David (1999), Static, dynamic, and hybrid neural networks in forecasting inflation, Computational economics, (14), pp.219-235.
Oussar, Y., I. Rivals, L. Personnaz, G.. Dreyfus (1998), Training wavelet networks for nonlinear dynamic input-output modeling, Neurocomputing, 20, pp.173-188.
Pan, Zuohong and Wang , Xiaodi (1998), A Stochastic Nonlinear Regression Estimator Using Wavelets, Computational economics, (11), pp.89-102.
Qian, Jun and Shao, Huihe (1999), Novel Algorithm of Wavelet Network Structural Design, Journal of Shanghai Jiaotong University, 33(4), pp.422-424.
Rao, Raghuveer M. and Bopardikar, Ajit S. (1998), Wavelet transforms: introduction to theory and applications, Addison Wesley Longman, Inc.
Strang, Gilbert and Nguyen, Truong (1996), Wavelet and filter banks, Wellesley-Cambridge Press,USA
Tseng, Fang-mei, Yu, Hsiao-cheng and Tzeng, Gwo-hsiung (2002), Combining neural network model with seasonal time series ARIMA model, Technological forecasting and social change (69),pp.71-87.
Verbeek, Marno (2000), A guide to modern econometrics, Chichester, Wiley, New York.
Wang, Jung-Hua; Leu, Jia-Yann (1996), Stock market trend prediction using ARIMA-based neural networks, 1996 IEEE International Conference on Neural Networks, (4), pp.2160 —2165.
Wang, Changzhou and Wang, Sean X. (2000) , Supporting content-based searches on time series via approximation, Scientific and Statistical Database Management, Proceedings. 12th International Conference, pp.69-81.
Whitehead, Bruce A. (1995), Neural, Parallel & Scientific Computations, 3(2), pp.273-280.
Yang, Yiwen, Liu, Guizhong, Zhang, Zongping (2000), Stock Market Trend Prediction Based on Neural Networks, Multiresolution Analysis and Dynamical Reconstruction, 6th Conference on Computational Intelligence for Financial Engineering (CIFEr), pp.155-157.
Yoon, Youngohc and Swales, George (1991), Predicting Stock Price Performance: A Neural Network Approach, Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Science.
Zhang, Qinghua and Benveniste, Albert (1992), Wavelet Networks, IEEE Transactions of Neural Networks, 3(6), pp.889-898.
Zhang, Jun, Walter, Gilbert G., Miao, Yubo, and Lee, Wan Ngai Wayne (1995), Wavelet Neural Networks for Function Learning, IEEE. Transactions on Signal Processing, 43(6), pp.1485-1497.
Zhang, Qinghua (1997), Using eavelet network in nonparametric estimation, IEEE Transcations on Neural Networks, 8(2), pp.227-236.
Zhang, Bai-Ling, Coggins, Richard, Jabri, Marwan Anwar, Dersch, Dominik, and Flower, Barry (2001), Multiresolution Forecasting for Futures Trading Using Wavelet Decompositions, IEEE Transactions on Neural Networks, 12(4).
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. 6. 吳慧珠,企業網路行銷策略之探討,清雲學報,民90.06 頁167-175。
2. 22.黃崇興 黃蘭貴,應用數據包絡法於航空公司航線經營績效之分析,管理學報,民89.03 頁149-181。
3. 13.周逸衡、蘇雲華,台灣服務行銷發展沿革之研究,企業管理學報,民國八十五年3月,第38期 ,第85---102頁。
4. 7. 李銓 李文瑞 許筱雯,產業競爭與經營績效--臺灣地區國際觀光旅館之策略群組分析,企業管理學報,民89.12 頁89-120。
5. 16.孫儷芳、戴輝煌,台灣地區定期船海運企業之行銷組合分析,國際航運管理研究,1999年,NOL 4。
6. 16.孫儷芳、戴輝煌,台灣地區定期船海運企業之行銷組合分析,國際航運管理研究,1999年,NOL 4。
7. 22.黃崇興 黃蘭貴,應用數據包絡法於航空公司航線經營績效之分析,管理學報,民89.03 頁149-181。
8. 21.莊耿銘,國際企業的行銷策略,商業職業教育,民84.04 頁19-24。
9. 21.莊耿銘,國際企業的行銷策略,商業職業教育,民84.04 頁19-24。
10. 13.周逸衡、蘇雲華,台灣服務行銷發展沿革之研究,企業管理學報,民國八十五年3月,第38期 ,第85---102頁。
11. 6. 吳慧珠,企業網路行銷策略之探討,清雲學報,民90.06 頁167-175。
12. 5. 李進成,行銷策略--名牌行銷的品牌定位策略規劃,戰略生產力雜誌,民85.10 頁98-101。
13. 5. 李進成,行銷策略--名牌行銷的品牌定位策略規劃,戰略生產力雜誌,民85.10 頁98-101。
14. 4. 呂鴻德 翟寶達,技術環境、策略配合對經營績效影響之研究,中原學報,民84.04 頁1-11。
15. 4. 呂鴻德 翟寶達,技術環境、策略配合對經營績效影響之研究,中原學報,民84.04 頁1-11。