| Abstract: | Generally, stock market prediction is a current renowned research topic. The existing prediction approaches are on the basis of the econometric and statistical approaches. Nevertheless, these approaches are complex to pact with non-stationary time series data. Thus, this study develops a new approach for stock market prediction utilizing a hybrid deep learning approach. In this work, the pre-processing stage is done initially with the assistance of yeo-jhonson transformation and padding-based Fourier transform (FT) denoising model. After that, technical indicators, like Williams’s %R, Rate of Change, Triple Exponential Moving Average (TRIX), Average Directional Index (ADX), Average True Range (ATR), and Relative Strength Index (RSI), are extracted. Subsequently, the feature fusion procedure is done by utilizing Morisita's overlap index and Deep Belief Network (DBN) model. Lastly, stock market forecasting is done by a hybrid approach integrating Deep Long Short-Term Memory (Deep LSTM) and Multi-Layer Perceptron (MLP). Moreover, the proposed model is compared with the conventional models, such as Padding-based Fourier Transform Denoising (P-FTD)+Recurrent Neural Network (RNN), Feedforward Neural Network (FNN)+Back-propagation Neural Network (BPNN), Residual-CNN-Seq2Seq (RCSNet), Long short-term memory (LSTM), Competitive Feedback Particle Swarm Optimization-based Deep Recurrent Neural Network (CFPSO-based Deep RNN) and Rider Deep LSTM & Deep RNN. Finally, the experimentation analysis states that the performance of Deep LSTM-MLP is superior to conventional approaches regarding the MSE, RMSE and MAE with the values of 0.113, 0.337, and 0.169. |
