Stock Price Prediction by Normalizing LSTM and GRU Models

Abstract

One of the financial world's most active markets is the stock market. The stock market, where investors can buy and sell shares, is crucial to economic prosperity. The objective of stock price prophecy is to forecast the worth of the firms’ monetary stocks in the succeeding years. Machine learning is a new concept that enables accurate and simple prediction. Due to the extreme unpredictability of the financial markets, there is a great deal of fragility and risk associated with them. Yet, because of the stock market's chaos, complexity, and dynamic nature, the assumption of a linear model may not be fair. Instead, it is more significant to build a more cohesive stock selection model by combining LSTM and GRU. All in all about suggested model, a nascent tactic for predicting stock prices for the next trading day is presented. It combines a significant learning approach with long short-term memory, a plan for specific neural frameworks, the assembly of gated recurrent units to the methodologies, auto in backward coordinators moving typical time procedure illustrations, and suspicions examination illustrations. To provide the most desired output, these algorithms have been merged in a feed-forward neural network structure.Stock markets relies on strong demand; equities with strong demand will rise in price while those with weak demand will fall in price. We offer a framework for analysing and predicting a company's future stock value that combines a GRU (Gated Recurrent Unit) method for calculating net growth with an LSTM (Long Short-Term Memory) model.