Classical Review On Stock Prognosis Using Long Short Term Memory

The main plot of this project is to compare different methods that primarily finds the stock prediction. There are so many machine learning and neurocomputing fields to predict the stock values. Stock price forecasting uses machine learning effectively. Various learning methods are available, including Moving Average (MA), K-nearest Neighbors (KNN), LSTM (Long Short Term Memory), and ARIMA.. LSTM is a type of ANN and RNN neural networks. Because in deep learning they are capable of storing the data in memory. But out of this Long Short-Term Memory(LSTM) is unique compares with other. Because it is used to create long term memory. LSTM performs better in big datasets. It has more additional space to store longer information and it stores longer period of time. While compare to other techniques they can’t store more data like LSTM does. In LSTM we use visualization method so, it is easy to compare the data and find the accuracy value. we used apple and google company datasets to perform LSTM. And here in the papers which we used as reference they performed on datasets namely- Chinese stock market data, Yahoo finance dataset(900000), BSE stocks Tick data, LM SCG, 4 year data of NASDAQ,100 stock market data of NASDAQ stocks. LSTM method gave us best accurate value so we choose LSTM method from others. The main objective of this paper is to find the accurate value of stock market using machine learning through best method out of all available. so we choose LSTM method.