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系統識別號 U0002-2907202010412800
中文論文名稱 基於融合長短期記憶及深度類神經網路的股票預測
英文論文名稱 A Stock Prediction based on the Fusion of Long Short-term Memory (LSTM) and Deep Neural Network
校院名稱 淡江大學
系所名稱(中) 資訊工程學系全英語碩士班
系所名稱(英) Master’s Program, Department of Computer Science and Information Engineering (English-taught program
學年度 108
學期 2
出版年 109
研究生中文姓名 程鈺翔
研究生英文姓名 Yu-Hsiang Cheng
學號 607780045
學位類別 碩士
語文別 英文
口試日期 2020-07-14
論文頁數 28頁
口試委員 指導教授-王英宏
委員-惠霖
委員-陳以錚
中文關鍵字 機器學習  循環神經網路  股票預測  時間序列預測 
英文關鍵字 Machine Learning  Recurrent Neural Network  Stock Prediction  Time Series Prediction 
學科別分類 學科別應用科學資訊工程
中文摘要 股票是近期最多人選擇的投資方式,然而對於股票新手第一次進場投資前多少有些不安和猶豫,除了培養一些基本觀念外,本文建立股票預測模型,預測下期股票收盤價,以提供給投資人做為買賣參考。本文提出的模型利用同產業股票漲跌會與龍頭股相似的特性,先是個別將股票歷年收盤價餵入(input)長短期記憶(LSTM)模型中學習,再將預測出的股票收盤價餵入(input)深度類神經網路(Deep Neural Network),使其學習最佳的權重分配,最終得到預測結果。
英文摘要 Recently, stocks are the most popular investment. However, for novice stock investor, they will be a little bit of anxiety and hesitation. In addition to cultivating some basic concepts, we establish a stock prediction model that predicted the stock close price of next day to provide investor a reference for trading. In this study, we propose that the same industry may have similar trend. First, we input the target and leading stock historical close prices to the Long Short-Term Memory (LSTM) models individually, and then input the predicted close price to a Deep Neural Network (DNN) to achieve the best weight distribution for learning, and finally get the prediction results.
論文目次 Table of Contents

Chinese Abstract................................I
Abstract........................................II
Table of Contents...............................IV
List of Figures.................................V
List of Tables..................................VI
Chapter 1 Introduction..........................1
Chapter 2 Related work..........................4
Chapter 3 Preliminary...........................8
Chapter 4 Proposed Stock Prediction Model.......9
4.1 LSTM_T and LSTM_L...........................10
4.2 LSTM_T and LSTM_L training..................13
4.3 DNN training................................14
4.4 Stock Prediction model......................15
Chapter 5 Performance Evaluation................16
5.1 Experiment Settings.........................16
5.2 Comparing Model Performance.................17
5.3 The Effectiveness of shifting...............19
5.4 Discussion of Parameter Settings............21
Chapter 6 Conclusion............................23
Reference.......................................24

List of Figures

Fig. 1. The stock price trend of 8046 and 3037............................................2
Fig. 2 shows the architecture of our propose model...........................................9
Fig. 3. The detail of LSTM_T and LSTM_L Training........................................12
Fig. 4. The concept of DNN......................14
Fig. 5. The closing price curve between leading stock and target stock from January 2010 to August 2019...16
Fig. 6. The curve of real closing price and predicted closing price from different models.............18
Fig. 7. The curve of real closing price and predicted closing price from different shifting day.......21
Fig. 8. The performance comparison of different parameter settings........................................22

List of Tables

Table 1: The Comparison of MAE..................18
Table 2: MAE in shifting D day..................19

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