Stock Market Price Prediction and Forecasting Using Stacked LSTM

Abstract

Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. Therefore, data scientists and analysts found that Deep Learning outperformed Machine Learning which is also proofed by all the collected research papers, and it is the most suitable methodologies to apply to the stock market forecasting domain. This paper explores different stacked LSTM Models for non-stationary financial time series in stock price prediction. This study is to predict stock market prices to make more acquaint and precise investment decisions. The experimental result will show that, this method can get quite accurate result, particularly effective in stock prediction. The proposed LSTM model will be designed to overcome gradient explosion, gradient vanishing, and save long-term memory. Firstly, The Model will have a comparative analysis between different LSTM models and integrating the model based on the result obtained from the analysis. The results will suggest that the developed stacked LSTM produces better predictive power and generalization. Key Words: stock market prediction, stacked LSTM, Deep Learning, Data pre-processing techniques, Data normalization, Neural Networks, Data Visualization, Training and Testing Set, Time series analysis, Predictive models, Future Forecasting.