Prediction Model for Stock Trading using Combined Long Short Term Memory and Neural Prophet with Regressors

Abstract

The prediction of the stock market offers information for the business to increase the profit in the share market. The prediction of a stock’s price is a challenging task because of the non-linearity, instability of time series information, and significant noise. In this research, an effective stock market prediction (SMP) is done by proposing the combination of long-short term memory (LSTM) and neural prophet (NP) namely the LSTMNP model. The interpretable features of NP are integrated with the LSTM to improve the prediction performances. On the other hand, the safety net incorporated in the LSTMNP is used to confirm the better prediction even if one of the models provides a lower performance. The main objective of this LSTMNP-SMP method is to improve the prediction with less errors. The LSTMNP-SMP method is analyzed using the parameters of mean absolute error (MAE), root-mean-square error (RMSE) and mean squared error (MSE). The existing approaches such as accelerated gradient-LSTM (AG-LSTM), support vector machine (SVM)-particle swarm optimization (PSO) and LSTM with artificial rabbits optimization (ARO) are used to compare the LSTMNP-SMP. The MSE of IBM for LSTMNP-SMP is 0.1110 which is less when compared to the AG-LSTM, SVM-PSO and LSTM-ARO.