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 本論文提出一個建立時間序列導向財經字典的框架。這部字典可以涵蓋不同型態的資源並且與預測問題的目標有明確的關係。在這個框架中的輸入是由文字資訊以及財經時間序列所組成。文字資訊像是財經新聞而財經時間序列像是一間公司的股價資訊。接著我們使用皮爾遜積差相關係數(Pearson product-momen correlation coefficient)來計算每個文字頻率時間序列以及一間公司的股票價格時間序列相關程度。使用皮爾遜相關係數來計算兩個時間序列的相關程度雖然是一個不錯的方法，但是當其中一個時間序列被延伸或是位移時，他的效果有其極限在。為了克服這個極限，我們採用動態時間校正(Dynamic Time Warping) 來解決這個問題。最後我們就能得所有與股價時間序列高相關的字來建立時間序列導向財務字典。此外我們利用所建立的字典來學習並建立一個簡單的股票走向預測模型。實驗結果顯示這些高相關的字普遍的擁有優良的預測能力，這證明通過其歷史股票價格捕捉一個公司的關鍵字提出這個想法的可行性。
 This thesis proposes a novel framework to build a time-series-oriented lexicon which can cover different types of sources and also has explicit links with the targets of prediction problems. In the framework, the input is composed of a text stream, such as financial news and a financial time series, such as the stock prices of a company. We then calculate the Pearson correlation between the frequency series of each word and the stock price series of a company. Although Pearson correlation gives a good idea of how much the two time series are correlated, it has a limitation in capturing the similarity when one of the series is stretched or shifted. To overcome this limitation, we adopt Dynamic time warping (DTW) to handle the problem. Finally, the words with high correlations will be extracted to build the time-series-oriented lexicon. Additionally, we adopt the learned lexicon to construct a model for stock price movement prediction. The experimental results demonstrate that the learned words generally have good prediction ability, which attests the practicability of the proposed idea of capturing a company's keywords via its historical stock prices.
 1 Introduction 12 Related Work 32.1 Soft and Hard Information . . . . . . . . . . . 32.2 Text Mining in Finance . . . . . . . . . . . . 42.3 Incorperating Hard Information into Text Mining . 43 Methodology 63.1 The Proposed Framework . . . . . . . . . .. . 63.2 Pearson Product-Moment Correlation Coefficient . . 73.3 Dynamic Time Warping (DTW) . . . . . . . .. . 94 Experiments on Time-Series-Oriented Lexicon Construction 124.1 Dataset . . . . . . . . . . . . . . . . . . . . 124.2 Data Preprocessing . . . . . . . . . . . . . . . .. . . . 134.2.1 Text Indexing . . . . . . . . . . . . . . . . . . . . . . 134.2.2 Dealing with Missing Data . . . . . . . . . . 134.3 Experimental Results: The Resulting Lexicons . . 145 Stock Price Movement Prediction via the Learned Lexicons 195.1 Dataset . . . . . . . . . . . .. . . . . . . . . 195.2 Experimental Setting . . . . . . . . . . . . . . 195.2.1 Feature Representation . . . . . . . . . . . 195.2.2 Parameter Setting . . . . . . . . . . . . . . . . . . . . . . 205.3 Experimental Results . . . . . . .. . . .. . . . 206 Conclusion and Future work 23
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 1 公司收購預測模型之研究-多變量CUSUM時間序列之應用 2 利用資料探勘技術於連鎖超商之營業額分析 3 台灣IPv4位址配發與亞太地區網路位址枯竭之預測 4 時間數列預測及決策支援系統-運用神經網路與基因演算法 5 以時間序列資料之模糊預測-以台中市空氣污染公開資料為例 6 智慧型建模及預測於養殖池水質評估 與機台狀況預測之應用 7 對交通資料之混合式預測 演算法

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