2 research outputs found
Bio-inspired algorithm optimization of neural network for the prediction of Dubai crude oil price
Previous studies proposed several bio-inspired algorithms for the optimization
of Neural Network (NN) to avoid local minima and to improve accuracy
and convergence speed. To advance the performance of NN, a new bio-inspired algorithm
called Flower Pollination Algorithm (FPA) is used to optimize the weights and
bias of NN due to its ability to explore very large search space and frequent chosen
of similar solution. The FPA optimized NN (FPNN) was applied to build a
model for the prediction of Dubai crude oil price unlike previous studies that mainly
focus on theWest Texas Intermediate and Brent crude oil price benchmarks. Result
Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory
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.Comment: 26 pages, 9 figure