文章基本信息

标题：3-WAY GATED RECURRENT UNIT NETWORK ARCHITECTURE FOR STOCK PRICE PREDICTION
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作者：Arjun Singh Saud ; Subarna Shakya
期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2021
卷号：12
期号：2
页码：421-427
DOI：10.21817/indjcse/2021/v12i2/211202011
出版社：Engg Journals Publications
摘要：Stock price prediction has been the aim of stock investors since the beginning, which is important for the investors to make rational decisions about buying and selling stocks. Nowadays deep learning techniques and technical indicators are popular tools among researchers for predicting stock prices. Mainly researchers from the field of computer science, Statistics, and finance are actively involving in this research field. This research paper proposed a 3-way gated recurrent unit (3-GRU) architecture to forecast the next day’s close price. The model is a combination of component GRU networks, where each component GRU network predicts the next day’s close price using a different set of technical indicators. The proposed 3-GRU model was evaluated by comparing its performance with all component GRU networks. Its performance was also compared with the GRU network that combines different sets of the technical indicators into one feature vector uses it to predict the next day’s close price. From the experimental results, we observed that the 3-GRU network architecture is able to predict the next day’s close price with lower mean squared error and greater consistency than other models and hence concluded that it is the better approach for predicting next day’s stock price.
关键词：3-Way Gated Recurrent Unit (3-GRU); Stock Price Prediction; Mean Average Convergence Divergence (MACD); Average Directional Index (ADX); Know Sure Things (KST).