臺灣博碩士論文加值系統

本研究主要引入線上機器學習於時間序利預測問題之應用。時間序列相關研究範圍廣泛且使用研究方法眾多，其中傳統機器學習演算法隨時間發展，需將舊資料與新資料合併，產生新的訓練資料，並針對整合過後的資料進行重新學習。而線上機器學習這類演算法，可僅根據新增之訓練資料，進行訓練模型更新，此特性使之十分適合運用在財務時間序列預測之應用。故本研究採用多種線上機器學習演算法，對於美國S&P 500指數成分股進行股價趨勢分析，並將模型預測與離線支援向量機預測之結果進行比較。實驗結果顯示，兩者準確度相當，但當訓練資料逐漸增加時，線上機器學習演算法即展現時間上的優勢。

This thesis studies online machine learning algorithms to the prediction problem for financial time series. Much research has been conducted in the field of time series analysis, in which many machine learning algorithms have been adopted to predict the trend of time series. However, as time goes by and more data become available, traditional machine learning algorithms need to merge the old data and the new one to a new training data, and re-train the model on the new training data. An online algorithm is one that can process its input piece-by-piece in a serial fashion, which makes it suitable for time series analysis. Therefore, in this study, we attempt to use several online machine learning algorithms to analyze the trend of S&P 500 index component stock prices. Experimental results show that there are small differences in terms of the accuracy between the offline and online algorithms; furthermore, the training time for the online learning algorithms is much faster than that for the offline algorithms, as the training data increases with time.

謝誌 4
摘要 5
Abstract 6
目次 i
圖次 iii
表次 iv
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第三節 研究內容與範圍 3
第四節 論文架構 4
第二章 文獻探討 6
第一節 支援向量機 6
第二節 感知器 8
第三節 股價趨勢預測相關學說 9
第四節 機器學習於財務方面相關研究 10
第三章 研究方法 11
第一節 樣本與資料來源 11
第二節 研究變數定義 12
第三節 研究工具及使用演算法 17
第四節 研究流程 18
第四章 研究結果與討論 20
第一節 預測準確率比較 20
第二節 運算時間比較 28
第五章 結論與建議 37
第一節 結論 37
第二節 建議 37
參考文獻 38

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