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研究生(外文):Lin, Yu-Fei
論文名稱(外文):Stock Index Forecast via a Recurrent Neural Network Base on the Zero-Crossing Rate Approach
指導教授(外文):Ueng, Yeong-Luh
口試委員(外文):Han, Chuan-HsiangChung, Wei-Ho
外文關鍵詞:Stock price predictionDeep learningRecurrent neural networkStandard & Poor's 500 stock indexDowJones' stock index
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By predicting the future stock price or index, such as the opening price and the closing price, we can place the long or short positions in advance. In addition to the stock index value, prediction on the positive or negative value of the difference between the closing price and the opening price is an important information for earning the profit. This paper presents a Recurrent Neural Network (RNN) based approach to forecast the opening price, the closing price and their difference. Compared to prior methods based on machine learning, our method puts greater focus on the pre-processing, such as normalized first order difference method, and the characteristics of the stock data, such as the zero-crossing rate (ZCR), which represents the ratio of data sign changes within a time interval. We propose a decision-making method based on an estimate of the ZCR to enhance the ability to predict the difference between the opening price and closing price. We apply our method to the Standard & Poor's 500 (S&P500) and the DowJones stock index. The results indicate that
our method can achieve better outcomes than prior works.
第一章 簡介 page 1
第二章 遞迴神經網路以及股市走勢的回顧 page 5
2.1 遞迴神經網路 page 5
2.2 長短期記憶 page 8
2.3 網路最佳化以及產生訓練集 page 10
2.4 股市中的價差 page 12
第三章 建立模型架構以及預測股市開盤、收盤以其價差 page 13
3.1 透過正規化之一階導數進行資料預處理以及預測 page 15
3.2 模型參數設定以及評估方法 page 17
3.3 針對股市價格的預測模擬評估結果 page 20
第四章 建立模型架構以及針對價差預測漲跌 page 29
4.1 利用股票趨勢以及過零率的決策方法 page 31
4.2 利用滑動窗口來估計過零率 page 34
4.3 針對漲跌的預測模擬評估 page 36
第五章 結論 page 42
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