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研究生： 李俊明
Abraham Koroh
論文名稱： Wavelet Transform for Predicting Stock Prices With LSTM-Attention Model
Wavelet Transform for Predicting Stock Prices With LSTM-Attention Model
指導教授： 呂永和
Yung-Ho Leu
口試委員: 楊維寧
Wei-Ning Yang
陳雲岫
Yun-Xiu Chen
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 86
中文關鍵詞： -
外文關鍵詞： ARIMA, Attention-LSTM, Inverse Transform
相關次數： 點閱：153  下載：1 
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  •  上一筆

    • Wavelet transform is one of the methods to extract the information of data by
    splitting the data into two parts, the approximate and detail. Approximate will capture the average of data while detail will capture the abrupt changes from the data. Such method is inspired from ARIMA where ARIMA also use the same approach by splitting the data into Auto-Regression and Moving Average part and then integrated the Auto-Regression and Moving Average. In ARIMA the Auto-Regression captured the regression part where data will predicted based on the previous data value while the moving average captured the error part of the data. This research has tested four models ANN, LSTM, Attention-LSTM and Inverse Transform. The stock data that is used for this research are the oil gas company and gold company stock prices. The company is also one of the biggest companies in the world. Those companies are Chevron, Exxon Mobil and Total S.A. for oil gas companies while Newmont, Barrick Gold and AngloGold Ashanti are the gold companies that used in this research. Based on the conducted experiment the best model is the Attention-LSTM model for predicting stock prices. However, the ARIMA model still performs better compared to Attention-LSTM. Still the difference is not very far and can have a better performance in the future.

    TABLE OF CONTENT Pages ABSTRACT............................................................................................................................i ACKNOWLEDGEMENT .....................................................................................................ii TABLE OF CONTENT....................................................................................................... iii LIST OF FIGURES ...............................................................................................................v LIST OF TABLES ...............................................................................................................vii Chapter 1. Introduction...............................................................................................1 1.1 Background of the Problem.....................................................................................1 1.2 Research Problem....................................................................................................4 1.3 Research Limitation ................................................................................................4 1.4 Objective .................................................................................................................4 1.5 Benefits of Research................................................................................................4 Chapter 2. Literature Review .....................................................................................6 Chapter 3. Theoritical Base ......................................................................................12 3.1 Stock......................................................................................................................12 3.2 Wavelet Transform................................................................................................12 3.3 Data Preprocessing ................................................................................................15 3.4 ARIMA Model ......................................................................................................16 3.5 Sliding Windows...................................................................................................16 3.6 Deep Learning .......................................................................................................17 3.7 Error Evaluation ....................................................................................................22 Chapter 4. Research Methodology ...........................................................................24 4.1 Research Setup ......................................................................................................24 4.2 System Description................................................................................................25 Chapter 5. Research Implementation .......................................................................38 5.1 Collecting Dataset .................................................................................................38 iv 5.2 Processing Data .....................................................................................................38 5.3 Experiment Result .................................................................................................42 Chapter 6. Conclusion and Future Works ................................................................46 6.1 Result.....................................................................................................................46 6.2 Future Works.........................................................................................................46 REFERENCES.....................................................................................................................47 APPENDIX.............................................................................................................................i

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