非對稱因果式類神經模糊系統於時間序列預測之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：3.145.66.215

姓名

陳郁晴(Yu-Ching Chen) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

非對稱因果式類神經模糊系統於時間序列預測之研究
(Time-series forecasting using neuro-fuzzy system with asymmetric causality)

相關論文

★ 變數選擇在智慧型系統與應用之研究	★ 智慧型系統之參數估測研究─一個新的DE方法
★ 合奏學習式智慧型系統在分類問題之研究	★ 複數模糊類神經系統於多類別分類問題之研究
★ 融入後設認知策略的複數模糊認知圖於分類問題之研究	★ 分類問題之研究－以複數型模糊類神經系統為方法
★ 智慧型差分自回歸移動平均模型於時間序列預測之研究	★ 計算智慧及複數模糊集於適應性影像處理之研究
★ 智慧型模糊類神經計算模式使用複數模糊集合與ARIMA模型	★ Empirical Study on IEEE 802.11 Wireless Signal – A Case Study at the NCU Campus
★ 自我建構式複數模糊ARIMA於指數波動預測之研究	★ 資料前處理之研究：以基因演算法為例
★ 針對文字分類的支援向量導向樣本選取	★ 智慧型區間預測之研究─以複數模糊類神經、支持向量迴歸、拔靴統計為方法
★ 複數模糊類神經網路在多目標財經預測	★ 智慧型模糊類神經計算使用非對稱模糊類神經網路系統與球型複數模糊集

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

時間序列資料的變化有眾多變因，在預測上一直是具有挑戰性的問題和研究。最常應用於股市上的股價變化，從時間的推移中找出股票之間的關係。本篇設計一多目標時間序列預測模型，應用於股票指數的預測。模型結合兩種模型架構，人工神經網路（Artificial Neural Networks, ANN）及球型複數神經模糊系統（Sphere complex neuro-fuzzy system, SCNFS），以進行多目標運算。本篇在SCNFS中加上箭靶層（Aim object layer），形成非對稱因果式的SCNFS。資料前處理使用多目標特徵挑選，以亂度熵（Entropy）的概念，從龐大的資料集中挑選出對目標具有貢獻的輸入資料。機器學習的部分使用混合式機器學習演算法，結合無導數最佳化演算法（Derivative-free optimization algorithm）及遞迴最小平方估計法（Recursive least squares estimation, RLSE），有效訓練模型的參數。本篇共進行三個實驗，實驗一以相同資料集同時進行單目標預測及多目標預測，驗證模型預測多個目標之能力。實驗二透過不同的機器學習演算法，進行相同資料集與模型架構的多目標預測，研究不同演算法之訓練效能。實驗三以相同的資料集及機器學習演算法，進行不同模型架構下之多目標預測，研究模型架構的優劣。

摘要(英)

Time-series forecasting is a challenge problem in research and application because there are many affecting factors in it. The most common application of time-series is the stock market. This research designed a kind of time-series forecasting model for multi-target to predict the stock index. In order to support multi-target operation, this model combines two kinds of structure: artificial neural networks (ANN) and sphere complex neuro-fuzzy system (SCNFS). The different part of SCNFS in this research is aim object layer (AOL). It can make SCNFS with asymmetric causality. In the part of data preprocessing, we use multi-target feature selection which extends from the concept of the entropy. It can select the features which contribute to the whole target from the large datasets. Moreover, we use hybrid machine learning algorithm, which efficiently trains the parameters through the derivative-free optimization algorithm and recursive least squares estimation (RLSE). At the end of this research, we did three experiments. The first experiment is to compare the results of single-target and multi-target forecasting trained by the same datasets, and to investigate whether our model can predict multiple target effectively. The second experiment uses the same datasets and model structure with different machine learning algorithms, and research the performance of different algorithms. The third experiment is to understand the performance of different model structure with the same datasets and machine learning algorithms.

關鍵字(中)

★ 多目標特徵挑選
★ 人工神經網路
★ 球型複數模糊集
★ 球型複數神經模糊系統
★ 混合式機器學習

關鍵字(英)

★ multi-target feature selection
★ artificial neural networks (ANN)
★ sphere complex fuzzy sets (SCFS)
★ sphere complex neuro-fuzzy system (SCNFS)
★ hybrid machine learning algorithm

論文目次

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
符號與專有名詞說明 viii
一、緒論 1
二、文獻探討 3
2-1 人工神經網路 3
2-2 模糊集合及模糊推論系統 3
2-3 機器學習演算法 4
2-4 特徵挑選 5
三、研究方法 6
3-1 多目標特徵挑選 7
3-2 球型複數模糊集（SCFS） 9
3-3 球型複數神經模糊系統（SCNFS） 10
3-3-1 建置模型架構 11
3-3-2 模型流程 12
3-4 混合式機器學習演算法 14
3-4-1 連續型蟻群演算法（CACO） 14
3-4-2 社會學習粒子群最佳化演算法（SL-PSO） 16
3-4-3 遞迴最小平方估計法（RLSE） 18
四、實驗結果 20
4-1 實驗一 21
4-2 實驗二 25
4-3 實驗三 29
五、結果與討論 33
六、結論與建議 38
參考文獻 39

參考文獻

[1] Wang, J. H., & Leu, J. Y. (1996, June). Stock market trend prediction using ARIMA-based neural networks. In Proceedings of International Conference on Neural Networks (ICNN′96) (Vol. 4, pp. 2160-2165). IEEE.
[2] Hassan, M. R., & Nath, B. (2005, September). Stock market forecasting using hidden Markov model: a new approach. In 5th International Conference on Intelligent Systems Design and Applications (ISDA′05) (pp. 192-196). IEEE.
[3] Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 23(8), 800-808.
[4] Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1643-1647). IEEE.
[5] Faustryjak, D., Jackowska-Strumiłło, L., & Majchrowicz, M. (2018, May). Forward forecast of stock prices using LSTM neural networks with statistical analysis of published messages. In 2018 International Interdisciplinary PhD Workshop (IIPhDW) (pp. 288-292). IEEE.
[6] McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
[7] Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.
[8] Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits (No. TR-1553-1). Stanford Univ Ca Stanford Electronics Labs.
[9] Winter, C. R., & Widrow, B. (1988, July). Madaline Rule II: a training algorithm for neural networks. In Secondf Annual International Conference on Neural Networks (pp. 1-401).
[10] Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Ph. D. dissertation, Harvard University.
[11] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
[12] Takagi, T., & Sugeno, M. (1993). Fuzzy identification of systems and its applications to modeling and control. In Readings in Fuzzy Sets for Intelligent Systems (pp. 387-403). Morgan Kaufmann.
[13] Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
[14] Hsia, T. C. (1977). System Identification: Least-Squares Methods (1st ed.). Lexington Books.
[15] Ramot, D., Milo, R., Friedman, M., & Kandel, A. (2002). Complex fuzzy sets. IEEE Transactions on Fuzzy Systems, 10(2), 171-186.
[16] Li, C., & Chiang, T. W. (2010, March). Complex neuro-fuzzy self-learning approach to function approximation. In Asian Conference on Intelligent Information and Database Systems(pp. 289-299). Springer, Berlin, Heidelberg.
[17] Tu, C. H., & Li, C. (2019). Multitarget prediction—A new approach using sphere complex fuzzy sets. Engineering Applications of Artificial Intelligence, 79, 45-57.
[18] Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
[19] Eberhart, R., & Kennedy, J. (1995, November). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (Vol. 4, pp. 1942-1948).
[20] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173.
[21] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, man, and cybernetics, Part B: Cybernetics, 26(1), 29-41.
[22] Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department.
[23] Cheng, R., & Jin, Y. (2015). A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences, 291, 43-60.
[24] Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67.
[25] Whitney, A. W. (1971). A direct method of nonparametric measurement selection. IEEE Transactions on Computers, 100(9), 1100-1103.
[26] Kwak, N., & Choi, C. H. (2002). Input feature selection by mutual information based on Parzen window. IEEE Transactions on Pattern Analysis & Machine Intelligence, (12), 1667-1671.
[27] Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
[28] Tu, C. H., & Li, C. (2017, April). A Novel Entropy-Based Approach to Feature Selection. In Asian Conference on Intelligent Information and Database Systems (pp. 445-454). Springer, Cham.
[29] Bowman, A. W., & Azzalini, A. (1997). Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations (Vol. 18). OUP Oxford.
[30] Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3), 267-278.
[31] Stigler, S. M. (1981). Gauss and the invention of least squares. The Annals of Statistics, 465-474.
[32] Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano.
[33] Chun, S. H., & Park, Y. J. (2006). A new hybrid data mining technique using a regression case based reasoning: Application to financial forecasting. Expert Systems with Applications, 31(2), 329-336.
[34] Lu, C. J. (2010). Integrating independent component analysis-based denoising scheme with neural network for stock price prediction. Expert Systems with Applications, 37(10), 7056-7064.
[35] Hsieh, T. J., Hsiao, H. F., & Yeh, W. C. (2011). Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied soft computing, 11(2), 2510-2525.
[36] 李俊賢、江泰緯（2013）。混合複數類神經模糊與自動回歸差分平均移動方法之智慧型時間序列預測模型。電子商務學報，15（1），137-158。
[37] Liu, W., & Morley, B. (2009). Volatility forecasting in the hang seng index using the GARCH approach. Asia-Pacific Financial Markets, 16(1), 51-63.
[38] Ye, F., Zhang, L., Zhang, D., Fujita, H., & Gong, Z. (2016). A novel forecasting method based on multi-order fuzzy time series and technical analysis. Information Sciences, 367, 41-57.
[39] Cheng, C. H., Chen, T. L., Teoh, H. J., & Chiang, C. H. (2008). Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Systems with Applications, 34(2), 1126-1132.
[40] Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, vol. 11, no. 5.

指導教授

李俊賢(Chunshien Li)

審核日期

2019-7-16

推文