论文标题
使用CNN和基于LSTM的深度学习模型对股票价格时间序列的强大分析
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models
论文作者
论文摘要
股票价格和股票价格变动模式的预测一直是研究的关键领域。尽管众所周知的有效市场假设排除了准确预测股票价格的任何可能性，但文献中有正式的主张证明了预测系统的准确建模，可以使我们能够以很高的准确性来预测股票价格。在本文中，我们提出了一系列基于深度学习的回归模型，这些模型在股票价格预测方面具有很高的准确性。为了构建我们的预测模型，我们使用在2012年12月31日至2015年1月9日的印度国家证券交易所（NSE）中列出的一家著名公司的历史股票价格数据。股票价格以一周的每个工作日的时间间隔五分钟记录。使用这些极详细的股票价格数据，我们建立了四个卷积神经网络（CNN）和五个基于短期和短期记忆（LSTM）的深度学习模型，以准确预测未来的股票价格。我们根据其执行时间及其根平方误（RMSE）值的所有建议模型的预测精度提供了详细的结果。
Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems that can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes intervals of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.
