MOBILE U-NET V3 AND BILSTM: PREDICTING STOCK MARKET PRICES BASED ON DEEP LEARNING APPROACHES

Abstract

The development of reliable stock market models has enabled investors to make better-informed decisions. Investors may be able to locate companies that offer the highest dividend yields and lower their investment risks by using a trading strategy. The degree to which stock prices are significantly correlated, however, makes stock market analysis more complicated when using batch processing methods. The stock market prediction has entered a time of advanced technology with the rise of technological wonders like global digitalization. The significance of artificial intelligence models has greatly increased as a result of the significantly enhance in market capitalization. Because it builds a strong time-series framework based on Deep Learning (DL) for predicting future stock prices, the proposed study is novel. Deep learning has recently enjoyed considerable success in some domains due to its exceptional capacity for handling data. For instance, it is commonly used in financial disciplines such as trade execution strategies, portfolio optimization, and stock market forecasting. In this research, we propose a structure based on Mobile U-Net V3 and a hybrid of a (Mobile U-Net V3-BiLSTM) with BiLSTM to forecast the closing prices of Apple, Inc. and S&P 500 stock data. The Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Pearson's Correlation (R), and Normalization Root Mean Squared Error (NRMSE) metrics were utilized to calculate the outcomes of the DL stock prediction methods. The Mobile U-Net V3-BiLSTM model outperformed other techniques for forecasting stock market prices.