Stock Price prediction with LSTM Based Deep Learning Techniques

Abstract

Stock price estimates are a complex task that requires a strong algorithm to calculate long-term prices. Stock prices are naturally related; hence it will be difficult to predict the cost. A proposed algorithm that uses market data to predict the share price using machine learning strategies such as a repetitive neural network called Long Short Term Memory, in which process weights are adjusted for each data point using a stochastic gradient. This program will provide better results compared to currently available pricing estimates algorithms. The network is trained and tested with a variety of input data to attract graphical results.