Deep Learning based Integrated Stacked Model for the Stock Market Prediction

Abstract

Recently, the stock market prediction has become one of the essential application areas of time-series forecasting research. The successful prediction of the stock market can be better guided to the investors to maximize their profit and to minimize the risk of investment. The stock market data are very much complex, non-linear and dynamic. Due to this reason, still, it is a challenging task. In recent time, deep learning method has become one of the most popular machine learning methods for time-series forecasting due to their temporal feature extraction capabilities. In this paper, we have proposed a novel Deep Learning-based Integrated Stacked Model (DISM) that integrates both the 1D Convolution neural network and LSTM recurrent neural network to find the spatial and temporal features from the stock market data. Our proposed DISM is applied to forecast the stock market. Here, we have also compared our proposed DISM with the single structured stacked LSTM, and 1D Convolution neural network models, and some other statistical models. We have observed that our proposed DISM produces better results in terms of accuracy and stability.