改良式複數模糊類神經系統於時間序列預測之應用

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姓名

吳清鈺(Ching-Yu Wu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

改良式複數模糊類神經系統於時間序列預測之應用
(Application of Modified Complex Neural Fuzzy System in Time Series Forecasting)

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摘要(中)

本研究針對時間序列預測之問題，提出了一個改良式的複數模糊類神經系統。改良的地方在於本研究根據模型輸出與目標間的誤差提出了一種誤差回饋的方式，藉由將誤差回饋到模型的後鑑部以改善模型預測時間序列的準確度。在模型輸入的選擇上，本研究基於夏農資訊熵(Shannon Entropy)的理論提出了多目標特徵挑選的方法，其主要是計算特徵對於目標所提供的資訊量，並以一套特徵選取策略針對特徵提供的資訊量來進行特徵的挑選。在模型建立上是使用結構學習(Structure learning)的方式，以數據本身客觀地建構模型，改變傳統以主觀的方式決定模型的建立。複數模糊集合(Complex fuzzy sets, CFSs)比以往傳統的模糊集合具有更佳的解釋能力，運用於模糊類神經系統(Neural fuzzy system)中能夠傳遞更大量的資訊，增加模型預測的效能，並且能夠讓模型同時預測多個目標。在模型參數學習階段，將粒子群最佳化演算法(Particle swarm optimization, PSO)與遞迴最小平方估計法(Recursive least squares estimator, RLSE)結合形成PSO-RLSE複合式演算法。PSO用於模型前鑑部之參數優化，而RLSE則負責模型後鑑部之參數更新，此種分而擊之的概念能夠降低模型參數的維度，提升找到模型參數最佳解的機率，並能降低模型整體的訓練時間。本篇研究使用財經市場的時間序列進行多目標預測的實驗，並從實驗結果顯示本論文所提出之模型與其他文獻相比有較好的預測結果。

摘要(英)

This study proposes a modified complex fuzzy neural system for the problem of time series prediction. The modified way is that this study proposes an error feedback method according to the errors between the model outputs and the targets. This paper feeds errors back to the model′s consequent part to increase the model prediction accuracy. In the selection of model input, this study proposes a multi-targets feature selection method based on the theory of Shannon entropy. The main purpose is to select features by calculating the amount of information provided by the features for the target, and using a set of feature selection strategies to do the multi-targets feature selection. In the establishment of the model, this paper changes the traditional subjective way to objective way by using the training data itself to construct the model setting. Complex fuzzy sets (CFSs) have better interpretive capabilities than traditional fuzzy sets. They can deliver a larger amount of information than traditional fuzzy sets in neural fuzzy systems, increase the effectiveness of model prediction, and let the model predict multi-targets at the same time. In the model parameter learning phase, Particle swarm optimization (PSO) and Recursive least squares estimator (RLSE) are combined to form a PSO-RLSE hybrid algorithm. PSO is used to optimize the parameters of the model′s premise part, while RLSE is responsible for the parameters of the model′s consequent part. This concept of divide-and-conquer can reduce the dimension of the model parameters, increase the probability of finding the best solution of the model parameters, and reduce the overall training time of the model. This study uses time series of the financial market to conduct the multi-targets forecasting experiments. The experimental results show that the model proposed in this paper has better forecasting abilities than other literatures.

關鍵字(中)

★ 多目標特徵挑選
★ 結構學習
★ 複數模糊類神經系統
★ PSO-RLSE複合式演算法
★ 誤差回饋
★ 時間序列預測

關鍵字(英)

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1.1 研究背景與目的 1
1.2 研究方法 2
1.3 論文架構 3
第二章文獻探討 4
2.1 特徵選取 4
2.2 模糊集合理論與模糊規則 5
2.3 複數模糊集合理論 8
2.4 模糊類神經系統 8
2.5 機器學習演算法 9
2.5.1 粒子群最佳化演算法 11
2.5.2 遞迴最小平方估計法 11
第三章系統設計與架構 13
3.1 多目標特徵挑選 13
3.2 結構學習 15
3.3 多目標複數模糊類神經模型 16
3.4 複合式PSO-RLSE演算法 21
第四章實驗實作與結果 23
4.1 實驗1: 單目標股票指數預測-SSECI(2006) 23
4.2 實驗2: 雙目標股票指數預測- DJIA(2004) 、TAIEX(2004) 27
4.3 實驗3: 四個目標股票指數預測- TAIEX(2006)、DJIA(2006)、Nikkei(2006)、FTSE(2006) 34
第五章實驗結果討論 43
第六章結論 51
6.1結論 51
6.2未來研究方向 52
參考文獻 53
附錄 57
實驗一 57
實驗二 58
實驗三 59

參考文獻

[1] M. Malliaris and L. Salchenberger, “Using neural networks to forecast the S&P 100 implied volatility,” Neurocomputing, vol. 10, iss. 2, pp. 183-195, March 1996.
[2] M. Khashei and M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Systems with Applications, vol. 37, iss. 1, pp. 479-489, January 2010.
[3] J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, iss. 3, pp. 665-685, May 1993.
[4] T. Marill and D. Green, “On the effectiveness of receptors in recognition systems,” IEEE Transactions on Information Theory, vol. 9, iss. 1, pp. 11-17, January 1963.
[5] A. W. Whitney, “A direct method of nonparametric measurement selection,” IEEE Transactions on Computers, vol. 20, iss. 9, pp. 1100-1103, September 1971.
[6] F. Li, Y. Yang and E. P. Xing, “From lasso regression to feature vector machine,” in Neural Information Processing Systems, pp. 779-786, January 2005.
[7] G. D. Tourassi, E. D. Frederick, M. K. Markey and C. E. Floyd, “Application of the mutual information criterion for feature selection in computer?aided diagnosis,” Medical Physics, vol. 28, iss. 12, pp. 2394-2402, December 2001.
[8] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, iss. 3, pp. 338-353, June 1965.
[9] E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1-13, 1975.
[10] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, iss. 1, pp. 116-132, January 1985.
[11] D. Ramot, R. Milo, M. Friedman and A. Kandel, “Complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 10, iss. 2, pp. 171-186, August 2002.
[12] C. T. Lin and C. S. G. Lee, “Neural-network-based fuzzy logic control and decision system,” IEEE Transactions on Computers, vol. 40, iss. 12, pp. 1320-1336, December 1991.
[13] I. Rojas, O. Valenzuela, F. Rojas, A. Guillen, L. J. Herrera, H. Pomares, L. Marquez and M. Pasadas, “Soft-computing techniques and ARMA model for time series prediction,” Neurocomputing, vol. 71, iss. 4-6, pp. 519-537, January 2008.
[14] A. Sharifian, M. J. Ghadi, S. Ghavidel, L. Li and J. Zhang, “A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data,” Renewable Energy, vol. 120, pp. 220-230, May 2018.
[15] J. Tang, F. Liu, Y. Zou, W. Zhang and Y. Wang, “An improved fuzzy neural network for traffic speed prediction considering periodic characteristic,” IEEE Transactions on Intelligent Transportation Systems, vol.18, iss. 9, pp. 2340-2350, September 2017.
[16] R. W. Kristjanpoller and V. K. Michell, “A stock market risk forecasting model through integration of switching regime, ANFIS and GARCH techniques,” Applied Soft Computing, vol. 67, pp. 106-116, June 2018.
[17] O. C. Yolcu and F. Alpaslan, “Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process,” Applied Soft Computing, vol. 66, pp. 18-33, May 2018.
[18] Y. Hou, L. Zhao and H. Lu, “Fuzzy neural network optimization and network traffic forecasting based on improved differential evolution,” Future Generation Computer Systems, vol. 81, pp. 425-432, April 2018.
[19] J. Kennedy and R. Eberhart, “Particle swarm optimization,” IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948, November 1995.
[20] J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River NJ: Prentice Hall, 1997.
[21] C. E. Shannon, “A Mathematical Theory of Communication,” The Bell System Technical Journal, vol. 27, pp. 379-423, July, 1948.
[22] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281-297, 1967.
[23] J. C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” Journal of Cybernetics, vol. 3, iss. 3, pp. 32-57, September 1973.
[24] J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Computers and Geosciences, vol. 10, iss. 2-3, pp. 191-203, 1984.
[25] S. L. Chiu, “Fuzzy model identification based on cluster estimation,” Journal of Intelligent and Fuzzy Systems, vol. 2, iss. 3, pp. 267-278, June 1994.
[26] K. Huarng and H. K. Yu, “A Type 2 fuzzy time series model for stock index forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 353, pp. 445-462, August 2005.
[27] C. H. Cheng and G. W. Cheng and J. W. Wang, “Multi-attribute fuzzy time series method based on fuzzy clustering,” Expert Systems with Applications, vol. 34, iss. 2, pp. 1235-1242, February 2008.
[28] S. M. Chen, “Forecasting enrollments based on high-order fuzzy time series,” Cybernetics and Systems, vol. 33, iss. 1, pp. 1-16, November 2002.
[29] L. W. Lee, L. H. Wang, S. M. Chen and Y. H. Leu, “Handling forecasting problems based on two-factors high-order fuzzy time series,” IEEE Transactions on Fuzzy Systems, vol. 14, iss. 3, pp. 468-477, June 2006.
[30] E. Egrioglu, C. H. Aladag, U. Yoclu, V. R. Uslu and N. A. Erillli, “Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering,” Expert Systems with Applications, vol. 38, iss. 8, pp. 10355-10357, August 2011.
[31] L. Wang, X. Liu and W. Pedrycz, “Effective intervals determined by information granules to improve forecasting in fuzzy time series,” Expert Systems with Applications, vol. 40, iss. 14, pp. 5673-5679, October 2013.
[32] E. Bas, U. Yoclu, E. Egrioglu and C. H. Aladag, “A fuzzy time series forecasting method based on operation of union and feed forward artificial neural network,” American Journal of Intelligent Systems, vol. 5, iss. 3, pp. 81-91, 2015.
[33] O. C. Yoclu, U. Yoclu, E. Egrioglu and C. H. Aladag, “High order fuzzy time series forecasting method based on an intersection operation,” Applied Mathematical Modeling, vol. 40, iss. 19-20, pp. 8750-8765, October 2016.
[34] W. Zhang, S. Zhang, S. Zhang, D. Yu and N. N. Huand, “A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search,” Neurocomputing, vol. 240, pp. 13-24, May 2017.
[35] C. Li and T. W. Chiang, “Complex neurofuzzy ARIMA forecasting—a new approach using complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 21, iss. 3, pp. 567-584, November 2012.
[36] K. H. Huarng, T. H. K. Yu and Y. W. Hsu, “A multivariate heuristic model for fuzzy time-series forecasting,” IEEE Transactions on Systems, Man and Cybernetics., vol. 37, iss. 4, pp. 836-846, July 2007.
[37] S. M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets System, vol. 81, iss. 3, pp. 311-319, August 1996.
[38] T. H. K. Yu and K. H. Huarng, “A bivariate fuzzy time series model to forecast the TAIEX,” Expert System with Applications, vol. 34, iss. 4, pp. 2945-2952, May 2008.
[39] T. H. K. Yu and K. H. Huarng, “Corrigendum to a bivariate fuzzy time series model to forecast the TAIEX,” Expert Systems with Applications, vol. 37, no. 7, p. 5529, July 2010.
[40] S. M. Chen and C.D. Chen, “TAIEX forecasting based on fuzzy time series and fuzzy variation groups,” IEEE Transactions on Fuzzy System, vol. 19, iss. 1, pp. 1-12, February 2011.
[41] H. J. Sadaei, R. Enayatifar, F. G. Guimaraes, M. Mahmud and Z. A. Alzamil, “Combining ARFIMA models and fuzzy time series for the forecast of long memory time series,” Neurocomputing, vol.175, pp. 782-796, January 2016.
[42] T. T. Hsieh, H. F. Hsiao and W. C. Yeh, “Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm,” Applied Soft Computing, vol. 11, iss. 2, pp.2510-2525, March 2011.
[43] 國立中央大學資訊管理所李俊賢教授, 研究生訓練課程內容2016-2018, 紀錄筆記。 (未發表)

指導教授

李俊賢

審核日期

2018-7-23

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