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研究生:林禹菲
研究生(外文):Lin, Yu-Fei
論文名稱:基於過零率方法的遞迴神經網路股票指數預測
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:翁詠祿
指導教授(外文):Ueng, Yeong-Luh
口試委員:韓傳祥鍾偉和
口試委員(外文):Han, Chuan-HsiangChung, Wei-Ho
口試日期:2019-02-26
學位類別:碩士
校院名稱:國立清華大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:45
中文關鍵詞:股票指數預測深度學習遞迴神經網路標準普爾500指數道瓊工業指數
外文關鍵詞:Stock price predictionDeep learningRecurrent neural networkStandard & Poor's 500 stock indexDowJones' stock index
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通過預測未來股票價格或是指數,例如開盤價或是收盤價,我們可以提前決定作多或是作空。除了股票指數的數值之外,對收盤價和開盤價之差的正值或是負值的預測是獲得利潤的重要訊息。本文提出了一種基於遞迴神經網路的方法來預測開盤價、收盤價以其兩者數值的差。與基於機器學習的現有方法相比,我們的方法鄭家專注於預處理,例如正規化的一階差分以及分析股票數據特性如過零率;一種代表了數據的符號在一個時間間隔內的變化率。我們提出了一種基於過零率估計的決策方法,以提高預測開盤價與收盤價之差的能力。我們將我們的方法應用於標準普爾500指數和道瓊工業指數。結果表明,我們的方法可以比以前的研究取得更好的結果。
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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