Comparative Analysis between LSTM and GRU in Stock Price Prediction

Abstract

Having the idea about stock price is not a big deal but investing the assets and maintaining profitably needs in-depth knowledge of shares, trading, stock exchanges, financial marketing, and activities. Without having prior knowledge of stocks, the person cannot invest the assets. The advantages of predicting in advance are if the stock price is high, it is acceptable to invest in the stocks, if the price is low, then it is not recommended to invest. Currently, there are many methods to achieve high accuracy of stock market trend prediction. To get benefit out of it, deep learning techniques can be used. There will be a lot of fluctuations when dealing with stock market data. When compared to machine learning, deep learning algorithms can solve better for non-linear problems. To forecast the stock price, the grated Recurrent Unit (GRU) approach is applied. Long Short-Term Memory (LSTM) and GRU are comparable, although GRU has fewer characteristics. GRU outperforms LSTM in terms of performance.