论文标题
部分可观测时空混沌系统的无模型预测
HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model
论文作者
论文摘要
股票市场是建立一个国家经济的支柱之一。多年来，人们正在投资股票市场，以从自己拥有的钱中获得尽可能多的利润。因此，拥有一个可以准确预测未来股票价格的预测模型至关重要。在机器学习的帮助下，如果正确建模的各种机器学习技术，这不是一项不可能的任务，这可能能够提供最佳的预测值。这将使投资者能够决定是否购买，出售或持有股票。本文的目的是通过提高准确性来预测公司金融股票的未来。在本文中，我们提出了使用历史数据和情感数据来通过应用LSTM进行有效预测股票价格的使用。通过分析情感分析领域的现有研究发现，股票价格的转移与新闻文章的发表之间存在很强的相关性。因此，在本文中，我们整合了这些因素，以更准确地预测股票价格。
One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machine learning, it is not an impossible task as the various machine learning techniques if modeled properly may be able to provide the best prediction values. This would enable the investors to decide whether to buy, sell or hold the share. The aim of this paper is to predict the future of the financial stocks of a company with improved accuracy. In this paper, we have proposed the use of historical as well as sentiment data to efficiently predict stock prices by applying LSTM. It has been found by analyzing the existing research in the area of sentiment analysis that there is a strong correlation between the movement of stock prices and the publication of news articles. Therefore, in this paper, we have integrated these factors to predict the stock prices more accurately.
