政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141045

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141045

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政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/141045

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141045

Title:	以星狀生成對抗網路結合系統工程與小波轉換學習動態時序性股票價量動態關係之股價預測 Use StarGAN based on ResNet and GRU Aggregating System Engineering to Learn the Joint Effect of Stock Price and Volume and Wavelet Transformation for Prediction of Stock Price
Authors:	李祈寬 Li, Chi-Kuan
Contributors:	姜國輝劉文卿 Jiang, Guo-Huei Liou, Wun-Cing 李祈寬 Li, Chi-Kuan
Keywords:	深度學習股價預測時序性神經網路田口方法小波轉換 Wavelet Transform Deep Learning Stock Price Prediction GRU Taguchi method
Date:	2022
Issue Date:	2022-08-01 17:25:24 (UTC+8)
Abstract:	本研究以星狀生成對抗網路將證券的量價關係進行深度學習訓練，並結合系統工程中的系統動態學，建立模擬證券市場的預測模型。運用星狀生成對抗網路多面向轉換特性可以成功的處理證券量價關係以提升預測的準確性。本研究將輸出的量價資料輸入時序性神經網路GRU預測模型，預測未來一交易日或五交易日的成交量與成交價資料，達到股價預測之目的。在深度學習中，參數的選擇採用田口實驗計畫法來選出最佳的參數組合，能大幅降低實驗次數與時間成本。本研究以小波轉換將時間域之資料轉換為頻率域之資料，並發現股票市場中的高頻與低頻之訊號。本研究以深度學習模型，拓展至時間域與頻率域之轉換，並成功找出兩者之間的轉換關係。 In this study, Star Generative Adversarial Network(Star-GAN) is used to conduct deep learning training on the volume-price relationship of securities and combined with the system dynamics in systems engineering. A prediction model for simulating the securities market is established. Using the multi-faceted transformation feature of Star-GAN can successfully deal with the relationship between securities volume and price to improve the prediction accuracy. In this study, the output volume and price data are input data into GRU network prediction model to predict the transaction volume and transaction price data for one or five trading days in the future. In deep learning, the selection of parameters adopts the Taguchi experimental method to select the best combination of parameters. Which can reduce the number of experiments and time costs. This research uses wavelet transform to convert the data in the time domain into the data in the frequency domain and finds the high-frequency and low-frequency signals in the stock market. In this study, the deep learning model is used to the conversion between the time domain and the frequency domain, and find out the conversion relationship between two domains.
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Description:	碩士國立政治大學資訊管理學系 109356040
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109356040
Data Type:	thesis
DOI:	10.6814/NCCU202201015
Appears in Collections:	[Department of MIS] Theses

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