论文标题
COVID-19对预测股票价格的影响：固定小波变换和双向长期记忆的整合
Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory
论文作者
论文摘要
Covid-19是一种传染病，主要影响呼吸系统。在进行这项研究时，有超过140万例Covid-19案例，最大的焦虑之一不仅是我们的健康，而且我们的生计也是如此。在这项研究中，作者研究了Covid-19对全球经济的影响，更具体地说，Covid-19对原油价格的金融运动和三个美国股票指数的影响：DJI，S＆P 500和NASDAQ COMPOSITES。预测商品和股票价格的拟议系统将固定小波变换（SWT）和双向长期记忆（BDLSTM）网络整合在一起。首先，SWT用于将数据分解为近似和细节系数。分解后，原油价格和股票市场指数的数据以及COVID-19确认的案例被用作未来价格变动预测的投入变量。结果，拟议的系统BDLSTM+WT-ADA在五天的原油价格预测方面取得了令人满意的结果。
COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on financial movement of Crude Oil price and three U.S. stock indexes: DJI, S&P 500 and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the Stationary Wavelet Transform (SWT) and Bidirectional Long Short-Term Memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM+WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.
