特徵選取對智慧型時間序列預測之效能研究

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：224

、訪客IP：3.135.193.179

姓名

陸怡廷(Yi-Ting Lu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

特徵選取對智慧型時間序列預測之效能研究
(Research on the Effectiveness of Feature Selection for Intelligent Time Series Prediction)

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)

在機器學習的過程中為正確反應出資料的全貌，使用大量的數據是不可或缺的，其代價即為運算資源的消耗與浪費，數據中的雜訊或極端值更會影響輸出結果的精確度。本研究提出一個多目標預測模型，透過計算特徵的資訊熵（Entropy）評估該特徵當中所涵蓋的資訊量，從原始數據中挑選出真正對預測目標產生有效貢獻的特徵作為機器學習的輸入值，藉此達到特徵挑選（Feature selection）的主要目的。除此之外，本研究採用球型複數類神經模糊系統（Sphere complex neuro-fuzzy system, SCNFS）搭配由鯨群演算法（Whale Optimization Algorithm, WOA）與遞迴最小平方估計法（Recursive least square estimator, RLSE）所構成的WOA-RLSE複合式學習演算法進行參數訓練，相較於自適應類神經模糊推論系統，SCNFS透過球型複數模糊集合乘載更多維度的資訊量，為多目標預測的主要核心；複合式學習演算法則是藉由將參數進行拆解之後分別以WOA與RLSE訓練之，使模型能夠更有效率地接近最佳解。綜合上述架構所組建而成的預測模型得以吸收多樣化的資料集並且同時預測兩個以上的時間序列數據，最後本文將此模型進一步應用於中國股市的預測，藉由多國金融資料預測多目標股票市場證實中國與他國股市的連動性。

摘要(英)

In the process of machine learning, in order to reflect the full picture of the data correctly, the use of a large amount of data is indispensable. Its cost is the consumption and waste of computing resources, the noise or extreme values in the data will affect the accuracy of the output. This study proposes a multi-target prediction model, which estimates the amount of information held by the feature by calculating the information entropy of the feature, and selects the feature that truly contributes to the predicted target from the original data as the input value for machine learning to achieve the main purpose of feature selection. In addition, this study uses the sphere complex neuro-fuzzy system(SCNFS) with a hybrid WOA-RLSE learning algorithm consist of the Whale optimization algorithm and recursive least square estimator, in the architecture of the training model. Compared with the adaptive neuro-fuzzy inference system, SCNFS multiplies the information volume of more dimensions through the sphere complex fuzzy set, which is the main core of multi-target prediction. The hybrid learning algorithm is based on the disassembled parameters, which is trained by WOA and RLSE respectively so that the model can approach the optimal solution more efficiently. The prediction model based on the above architecture can absorb diverse data sets and predict more than two time series data at the same time. Finally, this paper further applies this model to the forecasting of the Chinese stock market, to confirm the relationship between China and the international market by collecting international financial data and multi-target stock market prediction.

關鍵字(中)

★ 特徵選取
★ 球型複數模糊集
★ 球型複數模糊類神經系統
★ 鯨群演算法
★ 遞迴最小平方演算法
★ 多目標預測

關鍵字(英)

★ Feature selection
★ Sphere complex fuzzy set
★ Sphere complex neuro-fuzzy system
★ Whale optimization algorithm
★ Recursive least square estimator
★ Multi-target prediction

論文目次

中文摘要 i
英文摘要 ii
致謝 iii
目錄 iv
圖目錄 vi
符號與專有名詞說明 ix
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 研究方法概述 4
1.4 論文架構 4
第二章文獻探討 6
2.1 複數模糊集合 6
2.2 自適應類神經模糊推理系統 6
2.3 鯨群最佳化演算法 7
2.3.1 探索階段 8
2.3.2 搜尋階段 8
2.4 遞迴最小平方估計法 10
2.5 複合式學習演算法 11
第三章研究方法 12
3.1 特徵挑選 12
3.1.1 資料矩陣（Data matrix） 12
3.1.2 影響資訊矩陣（Influence information matrix） 13
3.1.3 多目標特徵選取（Multi-target feature selection） 17
3.2 球型複數模糊集合 18
3.3 球型複數類神經模糊系統 19
3.3.1 輸入層（Input layer） 20
3.3.2 球型複數模糊集合層（Sphere complex fuzzy layer） 20
3.3.3 前鑑部層（Premise layer） 21
3.3.4 正規化層（Normalization layer） 21
3.3.5 後鑑部層（Tagaki-Sugeno layer） 21
3.3.6 輸出層（Output layer） 21
3.4 WOA-RLSE複合式學習演算法 22
第四章實驗內容 25
4.1 特徵的擷取與影響 25
4.2 中國金融市場預測 28
4.3 中國與國際市場的相互作用 31
4.3.1 多目標預測與比較 31
4.3.2 多目標與單目標預測之差異性 35
第五章結果與討論 38
第六章結論與未來發展 40
參考文獻 41

參考文獻

[1] Jang, J.-S., “ANFIS: adaptive-network-based fuzzy inference system,” IEEE transactions on systems, man, and cybernetics, Vol.23, iss.3, pp.665-685, 1993.
[2] Jang, J.-S.R., et al., “Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence ” IEEE Transactions on automatic control, Vol.42, iss.10, pp.1482-1484, 1997.
[3] Khashei, M. & M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Systems with applications, Vol.37, iss.1, pp.479-489, 2010.
[4] Balkin, S.D. & J.K. Ord, “Automatic neural network modeling for univariate time series,” International Journal of Forecasting, Vol.16, iss.4, pp.509-515, 2000.
[5] Chung, H. & K.-s. Shin, “Genetic algorithm-optimized long short-term memory network for stock market prediction,” Sustainability, Vol.10, iss.10, pp.3765, 2018.
[6] Hsieh, T.-J., et al., “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, 2011.
[7] Qiu, M. & Y. Song, “Predicting the direction of stock market index movement using an optimized artificial neural network model,” PloS one, Vol.11, iss.5, pp.e0155133, 2016.
[8] Liu, Y., et al., “Stock volatility prediction using recurrent neural networks with sentiment analysis,” International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp.192-201, Springer, 2017
[9] Pang, X., et al., “An innovative neural network approach for stock market prediction,” The Journal of Supercomputing, pp.1-21, 2018.
[10] Li, X., et al., “An EPC forecasting method for stock index based on integrating empirical mode decomposition, SVM and cuckoo search algorithm,” Journal of Systems Science and Information, Vol.2, iss.6, pp.481-504, 2014.
[11] Wang, W. & H. Nie, “Comparative Study on Forecasting Model for Stock Index Future Price,” Modern Economy, Vol.9, iss.04, pp.750, 2018.
[12] Cheng, C.-H., et al., “Fuzzy time-series based on adaptive expectation model for TAIEX forecasting,” Expert systems with applications, Vol.34, iss.2, pp.1126-1132, 2008.
[13] Xi, L., et al., “A new constructive neural network method for noise processing and its application on stock market prediction,” Applied Soft Computing, Vol.15, pp.57-66, 2014.
[14] Bas, E., et al., “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.
[15] Ye, Q. & L. Wei, “The prediction of stock price based on improved wavelet neural network,” Open Journal of Applied Sciences, Vol.5, iss.04, pp.115-120, 2015.
[16] Khuat, T.T., et al., “Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm,” Journal of Telecommunications and Information Technology, iss.2, pp.43-52, 2016.
[17] Chen, Y.-S., et al., “A study of ANFIS-based multi-factor time series models for forecasting stock index,” Applied Intelligence, Vol.45, iss.2, pp.277-292, 2016.
[18] Zhang, W., et al., “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, 2017.
[19] Wei, Y., et al., “Prediction of Stock Price Trend Based on Wavelet Neural Network and RS Attributes Reduction,” 2017 International Conference on Education, Economics and Management Research (ICEEMR 2017), pp.95-98, Atlantis Press, 2017
[20] Chong, E., et al., “Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies,” Expert Systems with Applications, Vol.83, pp.187-205, 2017.
[21] Chatzis, S.P., et al., “Forecasting stock market crisis events using deep and statistical machine learning techniques,” Expert Systems with Applications, Vol.112, pp.353-371, 2018.
[22] Lei, L., “Wavelet neural network prediction method of stock price trend based on rough set attribute reduction,” Applied Soft Computing, Vol.62, pp.923-932, 2018.
[23] Shastri, M., et al., “Stock Price Prediction using Artificial Neural Model: An Application of Big Data,” ICST Transactions on Scalable Information Systems, Vol.6, iss.20, pp.1-8, 2019.
[24] Cao, W., et al., “Deep modeling complex couplings within financial markets,” Twenty-Ninth AAAI Conference on Artificial Intelligence, pp.2518-2524, 2015
[25] Zadeh, L.A., “Fuzzy sets,” Information and control, Vol.8, iss.3, pp.338-353, 1965.
[26] Ramot, D., et al., “Complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, Vol.10, iss.2, pp.171-186, 2002.
[27] Ramot, D., et al., “Complex fuzzy logic,” IEEE Transactions on Fuzzy Systems, Vol.11, iss.4, pp.450-461, 2003.
[28] Li, C. & T.-W. Chiang, “Complex neuro-fuzzy self-learning approach to function approximation,” Asian Conference on Intelligent Information and Database Systems, pp.289-299, Springer, 2010
[29] 張克群, et al., “適應性類神經模糊推論系統於財務危機預測之應用,” 臺灣銀行季刊, Vol.3, iss.61, pp.61-75, 2010.
[30] Takagi, T. & M. Sugeno, “Derivation of fuzzy control rules from human operator′s control actions,” IFAC Proceedings Volumes, Vol.16, iss.13, pp.55-60, 1983.
[31] Tu, C.-H. & C. Li, “Multiple Function Approximation-A New Approach Using Complex Fuzzy Inference System,” Asian Conference on Intelligent Information and Database Systems, pp.243-254, Springer, 2018
[32] Mirjalili, S. & A. Lewis, “The whale optimization algorithm,” Advances in engineering software, Vol.95, pp.51-67, 2016.
[33] Li, C. & T. Wu, “Adaptive fuzzy approach to function approximation with PSO and RLSE,” Expert Systems with Applications, Vol.38, iss.10, pp.13266-13273, 2011.
[34] 李俊賢 & 江泰緯, “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, Vol.15, iss.1, pp.137-158, 2013.
[35] 劉福才, et al., “基于智能优化算法的 TS 模糊模型辨识,” 系统工程与电子技术, Vol.35, iss.12, pp.2643-2650, 2010.
[36] Tu, C.-H. & C. Li, “A Novel Entropy-Based Approach to Feature Selection,” Asian Conference on Intelligent Information and Database Systems, pp.445-454, Springer, 2017
[37] Shannon, C.E., “A mathematical theory of communication,” Bell system technical journal, Vol.27, iss.3, pp.379-423, 1948.
[38] Girolami, M. & C. He, “Probability density estimation from optimally condensed data samples,” IEEE Transactions on pattern analysis and machine intelligence, Vol.25, iss.10, pp.1253-1264, 2003.

指導教授

李俊賢(Chunshien Li)

審核日期

2019-7-16

推文