Deep Neural Network for Multivariate Time-Series Forecasting

Abstract

Recently, Deep Neural Network (DNN) architecture with a deep learning approach has become one of the robust techniques for time-series forecasting. Although DNNs provide fair forecasting results for the time-series prediction, still they are suffering from various challenges. Because most of the time-series data, especially the financial time-series data are multidimensional, dynamic, and nonlinear. Hence, to address these challenges, here, we have proposed a new deep learning model, Stacked Long Short-Term Memory (S-LSTM) model to forecast the multivariate time-series data. The proposed S-LSTM model is constructed by the stacking of multiple Long Short-Term Memory (LSTM) units. In this research work, we have used six different data normalization techniques to normalize the dataset as the preprocessing step of the deep learning methods. Here, to evaluate and analyze the performance of our proposed model S-LSTM, we have used the multivariate financial time-series data, such as stock market data. We have collected these data from two stock exchanges, namely, Bombay Stock Exchange (BSE) and New York Stock Exchange (NYSE). The experimental results show that the prediction performance of the S-LSTM model can be improved with the appropriate selection of the data normalization technique. The results also show that the prediction accuracy of the S-LSTM model is higher than the other well-known methods.