學術產出-學位論文
文章檢視/開啟
書目匯出
-
題名 訊號分解對於集成學習預測股價準確率之影響—以台灣加權股價指數為例
Influence of Signal Decomposition on the Accuracy of Ensemble Learning to Predict Stock Price: Taking TAIEX as an Example作者 錢慧娟
Chien, Hui-Chuan貢獻者 黃泓智
Huang, Hong-Chih
錢慧娟
Chien, Hui-Chuan關鍵詞 台灣加權股價指數
變分模態分解
經驗模態分解
極限學習機
改良式和弦搜尋優化演算法
集成學習
TAIEX
Variational Mode Decomposition (VMD)
Empirical Mode Decomposition (EMD)
Extreme Learning Machine (ELM)
Improved Harmony Search Algorithm (IHS)
Ensemble Learning日期 2022 上傳時間 1-八月-2022 17:32:26 (UTC+8) 摘要 股價為一高噪音、非線性和非平穩的時間序列資料，因此股價預測長期以來均是一項具有挑戰性之熱門研究。本文提出一基於變分模態分解 (Variational Mode Decomposition, VMD)和經驗模態分解 (Empirical Mode Decomposition, EMD)之二次分解技術，結合極限學習機 (Extreme Learning Machines, ELM)和改良式和弦搜尋優化演算法 (Improved Harmony Search Algorithm, IHS)之二階段混合模型，並利用此混合模型預測台灣加權股價指數之股價。本文將VMD分解技術應用於分解台灣加權股價指數之收盤價，取得多個子序列和噪音項後，再將EMD分解技術應用於分解噪音項，最後將子序列和由台灣加權股價指數衍生出之技術指標透過ELM模型得出初步預測結果，再以IHS演算法整合並優化最終結果。而為驗證模型的有效性，本文將此混合模型和單一ELM模型以及單一VMD分解技術之混合模型進行比較，並比較預測一日、三日和五日之結果。實證結果顯示，本文所提出之混合模型無論在短天期或是長天期，均具有較好的預測效果，其中二次分解技術優於一次分解技術之結果亦說明：深入分析噪音項所含之有效資訊，不僅更完善的捕捉原始序列的特徵，亦更有效地提升模型的預測能力。
As stock data is characterized by high-noise, non-linear, and non-stationary, predicting stock price is usually subject to a main challenge. In this study, to enhance the predictive performance, we proposed a new two-stage hybrid model by combining with extreme learning machine (ELM) and improved harmony search algorithm (IHS) which based on the secondary decomposition technique of variational mode decomposition (VMD) and empirical mode decomposition (EMD), named VMD-EMD-ELM-IHS model. The hybrid model applies VMD techniques to the original closing price of TAIEX to obtain different subsequences and the residual term, then applies EMD techniques to the residual term, then predicts all subsequences and technical analysis indicators by ELM models, and then applies IHS to integrate the prediction results of ELM models to obtain the final prediction results. To verify the performance and robustness of the hybrid model, the results were compared with other models, including single ELM model, and VMD-ELM-IHS model, and respectively, tested by one-step, three-step, and five-step forward forecasting. The empirical results show that the hybrid model we proposed achieves the best prediction performance in other models and all prediction scenarios. Also, the secondary decomposition technique superior to the single decomposition technique shows that fully considering the residual term not only captures the characteristics of the original sequence but also effectively improves the prediction accuracy.參考文獻 蜂行資本 (Hive Ventures)有限公司 (2022)。2022台灣企業AI趨勢報告。Ariyo, Adebiyi A., Adewumi, Adewumi O., & Ayo, Charles K. (2014). Stock price prediction using the ARIMA model. IEEE, 106–112. doi:10.1109/uksim.2014.67Arowolo, W. B. (2013). Predicting stock prices returns using GARCH model. The International Journal of Engineering and Science (IJES), 2(5), 32-37.Assad, A., & Deep, K. (2018). A hybrid harmony search and simulated annealing algorithm for continuous optimization. Information Sciences, 450, 246–266.Baştanlar, Y., & Özuysal, M. (2013). Introduction to Machine Learning. Methods in Molecular Biology, 105–128. doi:10.1007/978-1-62703-748-8_7Bisoi, R., Dash, P., & Parida, A. (2019). Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis. Applied Soft Computing, 74, 652–678.Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308.Brown, R. G., & Meyer, R. F. (1961). The fundamental theorem of exponential smoothing. Operations Research, 9(5), 673–685. doi: 10.1287/opre.9.5.673Bughin J., Seong J., Manyika J., Chui M., Joshi R. (2018, September). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute.Cheng, C. H., & Wei, L. Y. (2014). A novel time-series model based on empirical mode decomposition for forecasting TAIEX. Economic Modelling, 36, 136–141.David M. Q. Nelson, Adriano C. M. Pereira, & Renato A. de Oliveira (2017). Stock market`s price movement prediction with LSTM neural networks. IEEE. doi: 10.1109/IJCNN.2017.7966019Dragomiretskiy, K., & Zosso D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.Dutta, G., Jha, P., Laha, A. K., & Mohan, N. (2006). Artificial neural network models for forecasting stock price index in the Bombay stock exchange. Journal of Emerging Market Finance, 5(3), 283–295. doi: 10.1177/097265270600500305Gogna, A., & Tayal, A. (2013). Metaheuristics: review and application. Journal of Experimental & Theoretical Artificial Intelligence, 25(4), 503–526.H., Singh Rupal, Mohanty, Soumya R., Kishor, N., Singh, & Dushyant K. (2019). Comparison of empirical mode decomposition and wavelet transform for power quality assessment in FPGA. IEEE, 1–6. doi: 10.1109/PEDES.2018.8707444Harvey, A. (2006). Handbook of Economic Forecasting (1st ed.), 1(1). 327–412. doi:10.1016/S1574-0706(05)01007-4Hosni, M., Idri, A., Nassif, A., & Abran, A. (2016). Heterogeneous ensembles for software development effort estimation. IEEE, 174–178.Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. IEEE, 2, 985–990.Huang, G., Huang, G.B., Song, S., & You, K. (2015). Trends in extreme learning machines: A review. Neural Networks, 61, 32–48.Huang, G., Zhu, Q., & Siew, C. (2006). Extreme learning machine: Theory and applications designs. Neurocomputing, 70(1), 489–501.Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings A, 454(1971), 903–995. doi: 10.1098/rspa.1998.0193Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307–319. doi: 10.1016/s0925-2312(03)00372-2Kim, T., & Kim, H. Y. (2019). Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PLOS ONE, 14(2), doi: 10.1371/journal.pone.0212320Lahmiri, S. (2016). Intraday stock price forecasting based on variational mode decomposition. Journal of Computational Science, 12, 23–27.Lazzeri, F. (2020). Machine Learning for Time Series Forecasting with Python (1st ed.). Wiley Publishers. 61-99. doi: 10.1002/9781119682394.ch3Moore, E.H. (1920). On the Reciprocal of the General Algebraic Matrix. Bulletin of American Mathematical Society, 26, 394-395.Shalabi, L., & Shaaban, Z. (2006). Normalization as a preprocessing engine for data mining and the approach of preference matrix. IEEE Computer Society, 207–214. doi: 10.1109/depcos-relcomex.2006.38Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017). Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Applied Energy, 190, 390–407. doi: 10.1016/j.apenergy.2016.12.134Wang, R., Kwong, S., & Wang, Debby D. (2013). An analysis of elm approximate error based on random weight matrix. International Journal of Uncertainty, Fuzziness, and Knowledge-Based systems, 21(2), 1–12.Wei, L. Y. (2016). A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing, 42(C), 368–376.Zhang, X., Shi, J., Wang, D., & Fang, B. (2018). Exploiting investors social network for stock prediction in China’s market. Journal of Computational Science, 28, 294–303.Zou, D., Gao, L., Wu, J., Li, S., Li, Y. (2010). A novel global harmony search algorithm for reliability problems. Computers & Industrial Engineering, 58(2), 307–316. 描述 碩士
國立政治大學
風險管理與保險學系
109358014資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109358014 資料類型 thesis dc.contributor.advisor 黃泓智 zh_TW dc.contributor.advisor Huang, Hong-Chih en_US dc.contributor.author (作者) 錢慧娟 zh_TW dc.contributor.author (作者) Chien, Hui-Chuan en_US dc.creator (作者) 錢慧娟 zh_TW dc.creator (作者) Chien, Hui-Chuan en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-八月-2022 17:32:26 (UTC+8) - dc.date.available 1-八月-2022 17:32:26 (UTC+8) - dc.date.issued (上傳時間) 1-八月-2022 17:32:26 (UTC+8) - dc.identifier (其他 識別碼) G0109358014 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141077 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 風險管理與保險學系 zh_TW dc.description (描述) 109358014 zh_TW dc.description.abstract (摘要) 股價為一高噪音、非線性和非平穩的時間序列資料，因此股價預測長期以來均是一項具有挑戰性之熱門研究。本文提出一基於變分模態分解 (Variational Mode Decomposition, VMD)和經驗模態分解 (Empirical Mode Decomposition, EMD)之二次分解技術，結合極限學習機 (Extreme Learning Machines, ELM)和改良式和弦搜尋優化演算法 (Improved Harmony Search Algorithm, IHS)之二階段混合模型，並利用此混合模型預測台灣加權股價指數之股價。本文將VMD分解技術應用於分解台灣加權股價指數之收盤價，取得多個子序列和噪音項後，再將EMD分解技術應用於分解噪音項，最後將子序列和由台灣加權股價指數衍生出之技術指標透過ELM模型得出初步預測結果，再以IHS演算法整合並優化最終結果。而為驗證模型的有效性，本文將此混合模型和單一ELM模型以及單一VMD分解技術之混合模型進行比較，並比較預測一日、三日和五日之結果。實證結果顯示，本文所提出之混合模型無論在短天期或是長天期，均具有較好的預測效果，其中二次分解技術優於一次分解技術之結果亦說明：深入分析噪音項所含之有效資訊，不僅更完善的捕捉原始序列的特徵，亦更有效地提升模型的預測能力。 zh_TW dc.description.abstract (摘要) As stock data is characterized by high-noise, non-linear, and non-stationary, predicting stock price is usually subject to a main challenge. In this study, to enhance the predictive performance, we proposed a new two-stage hybrid model by combining with extreme learning machine (ELM) and improved harmony search algorithm (IHS) which based on the secondary decomposition technique of variational mode decomposition (VMD) and empirical mode decomposition (EMD), named VMD-EMD-ELM-IHS model. The hybrid model applies VMD techniques to the original closing price of TAIEX to obtain different subsequences and the residual term, then applies EMD techniques to the residual term, then predicts all subsequences and technical analysis indicators by ELM models, and then applies IHS to integrate the prediction results of ELM models to obtain the final prediction results. To verify the performance and robustness of the hybrid model, the results were compared with other models, including single ELM model, and VMD-ELM-IHS model, and respectively, tested by one-step, three-step, and five-step forward forecasting. The empirical results show that the hybrid model we proposed achieves the best prediction performance in other models and all prediction scenarios. Also, the secondary decomposition technique superior to the single decomposition technique shows that fully considering the residual term not only captures the characteristics of the original sequence but also effectively improves the prediction accuracy. en_US dc.description.tableofcontents 第一章 緒論 7第一節 研究動機與背景 7第二節 研究目的 9第三節 研究流程 10第二章 文獻探討 11第一節 時間序列資料預處理文獻探討 11第二節 時間序列資料訊號分解文獻探討 12第三節 機器學習模型文獻探討 14第四節 啟發式優化演算法文獻探討 15第三章 研究方法 17第一節 研究架構 17第二節 資料預處理與特徵值之生成 19第三節 時間序列資料訊號分解 21第四節 機器學習模型 27第五節 啟發式優化演算法 36第六節 集成模型之建構 41第七節 評估預測誤差之指標 44第四章 實證結果 45第五章 結論與建議 55參考文獻 57附錄一：技術指標之定義與說明 60 zh_TW dc.format.extent 8224358 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109358014 en_US dc.subject (關鍵詞) 台灣加權股價指數 zh_TW dc.subject (關鍵詞) 變分模態分解 zh_TW dc.subject (關鍵詞) 經驗模態分解 zh_TW dc.subject (關鍵詞) 極限學習機 zh_TW dc.subject (關鍵詞) 改良式和弦搜尋優化演算法 zh_TW dc.subject (關鍵詞) 集成學習 zh_TW dc.subject (關鍵詞) TAIEX en_US dc.subject (關鍵詞) Variational Mode Decomposition (VMD) en_US dc.subject (關鍵詞) Empirical Mode Decomposition (EMD) en_US dc.subject (關鍵詞) Extreme Learning Machine (ELM) en_US dc.subject (關鍵詞) Improved Harmony Search Algorithm (IHS) en_US dc.subject (關鍵詞) Ensemble Learning en_US dc.title (題名) 訊號分解對於集成學習預測股價準確率之影響—以台灣加權股價指數為例 zh_TW dc.title (題名) Influence of Signal Decomposition on the Accuracy of Ensemble Learning to Predict Stock Price: Taking TAIEX as an Example en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 蜂行資本 (Hive Ventures)有限公司 (2022)。2022台灣企業AI趨勢報告。Ariyo, Adebiyi A., Adewumi, Adewumi O., & Ayo, Charles K. (2014). Stock price prediction using the ARIMA model. IEEE, 106–112. doi:10.1109/uksim.2014.67Arowolo, W. B. (2013). Predicting stock prices returns using GARCH model. The International Journal of Engineering and Science (IJES), 2(5), 32-37.Assad, A., & Deep, K. (2018). A hybrid harmony search and simulated annealing algorithm for continuous optimization. Information Sciences, 450, 246–266.Baştanlar, Y., & Özuysal, M. (2013). Introduction to Machine Learning. Methods in Molecular Biology, 105–128. doi:10.1007/978-1-62703-748-8_7Bisoi, R., Dash, P., & Parida, A. (2019). Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis. Applied Soft Computing, 74, 652–678.Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308.Brown, R. G., & Meyer, R. F. (1961). The fundamental theorem of exponential smoothing. Operations Research, 9(5), 673–685. doi: 10.1287/opre.9.5.673Bughin J., Seong J., Manyika J., Chui M., Joshi R. (2018, September). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute.Cheng, C. H., & Wei, L. Y. (2014). A novel time-series model based on empirical mode decomposition for forecasting TAIEX. Economic Modelling, 36, 136–141.David M. Q. Nelson, Adriano C. M. Pereira, & Renato A. de Oliveira (2017). Stock market`s price movement prediction with LSTM neural networks. IEEE. doi: 10.1109/IJCNN.2017.7966019Dragomiretskiy, K., & Zosso D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.Dutta, G., Jha, P., Laha, A. K., & Mohan, N. (2006). Artificial neural network models for forecasting stock price index in the Bombay stock exchange. Journal of Emerging Market Finance, 5(3), 283–295. doi: 10.1177/097265270600500305Gogna, A., & Tayal, A. (2013). Metaheuristics: review and application. Journal of Experimental & Theoretical Artificial Intelligence, 25(4), 503–526.H., Singh Rupal, Mohanty, Soumya R., Kishor, N., Singh, & Dushyant K. (2019). Comparison of empirical mode decomposition and wavelet transform for power quality assessment in FPGA. IEEE, 1–6. doi: 10.1109/PEDES.2018.8707444Harvey, A. (2006). Handbook of Economic Forecasting (1st ed.), 1(1). 327–412. doi:10.1016/S1574-0706(05)01007-4Hosni, M., Idri, A., Nassif, A., & Abran, A. (2016). Heterogeneous ensembles for software development effort estimation. IEEE, 174–178.Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. IEEE, 2, 985–990.Huang, G., Huang, G.B., Song, S., & You, K. (2015). Trends in extreme learning machines: A review. Neural Networks, 61, 32–48.Huang, G., Zhu, Q., & Siew, C. (2006). Extreme learning machine: Theory and applications designs. Neurocomputing, 70(1), 489–501.Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings A, 454(1971), 903–995. doi: 10.1098/rspa.1998.0193Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307–319. doi: 10.1016/s0925-2312(03)00372-2Kim, T., & Kim, H. Y. (2019). Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PLOS ONE, 14(2), doi: 10.1371/journal.pone.0212320Lahmiri, S. (2016). Intraday stock price forecasting based on variational mode decomposition. Journal of Computational Science, 12, 23–27.Lazzeri, F. (2020). Machine Learning for Time Series Forecasting with Python (1st ed.). Wiley Publishers. 61-99. doi: 10.1002/9781119682394.ch3Moore, E.H. (1920). On the Reciprocal of the General Algebraic Matrix. Bulletin of American Mathematical Society, 26, 394-395.Shalabi, L., & Shaaban, Z. (2006). Normalization as a preprocessing engine for data mining and the approach of preference matrix. IEEE Computer Society, 207–214. doi: 10.1109/depcos-relcomex.2006.38Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017). Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Applied Energy, 190, 390–407. doi: 10.1016/j.apenergy.2016.12.134Wang, R., Kwong, S., & Wang, Debby D. (2013). An analysis of elm approximate error based on random weight matrix. International Journal of Uncertainty, Fuzziness, and Knowledge-Based systems, 21(2), 1–12.Wei, L. Y. (2016). A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing, 42(C), 368–376.Zhang, X., Shi, J., Wang, D., & Fang, B. (2018). Exploiting investors social network for stock prediction in China’s market. Journal of Computational Science, 28, 294–303.Zou, D., Gao, L., Wu, J., Li, S., Li, Y. (2010). A novel global harmony search algorithm for reliability problems. Computers & Industrial Engineering, 58(2), 307–316. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200961 en_US
