政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/138941

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/138941

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题名:	時間序列特徵學習應用於股票市場預測 Time Series Representation Learning for Stock Market Prediction
作者:	焉然 Yen, Jan
贡献者:	蔡炎龍 Tsai, Yen-Lung 焉然 Yen, Jan
关键词:	深度學習卷積神經網路長短期記憶神經網路孿生神經網路特徵學習對比學習ｐ進數碎形ｐ進位表示法股票市場預測 Deep Learning CNN LSTM Siamese Network Representation Learning Contrastive Learning p-adic Number Fractal p-adic Representation Stock Market Prediction
日期:	2021
上传时间:	2022-02-10 13:06:52 (UTC+8)
摘要:	特徵學習是當今深度學習的熱門議題，但目前大多數的特徵學習都是針對圖像或是自然語言處理。本文嘗試利用特徵學習的方法，對時間序列資料做特徵學習。並以股票資料為主要應用。本文的特徵學習主要採用孿生神經網路做對比學習，以找到最佳的特徵學習函數。我們也嘗試結合以p進數表示的股價資訊的方法來輔助對比學習的訓練。我們發現就股票預測問題而言，利用特徵學習訓練的模型相對於單純的卷積神經網路或是長短期記憶神經網路預測出來的結果穩定，而且結合碎形p進位表示法所訓練出來的結果是最好的。 Representation learning has become a popular mehthod in deep learning. However, most of applications and researches of it are image recognition or natural language process. In this paper, we try to apply representation learning method to time-series data such as stock. We propose a SiamCL model to implement contrastive representation learning with Siamese network. With this model, our goal is to find the most suitable representation of data. We also combine the fractal p-adic representation to improve the performance of models. We find the fact that SiamCL is rather stable than CNN and LSTM. Moreover, when dealing with extremely imbalanced dataset, SiamCL is more powerful and fractal p-adic representation indeed can improve the performance of models.
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描述:	碩士國立政治大學應用數學系 105751005
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0105751005
数据类型:	thesis
DOI:	10.6814/NCCU202200038
显示于类别:	[應用數學系] 學位論文

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