Title: DLSTMFRNN - a newly developed network-based deep long short-term memory and recurrent neural network for stock market prediction

Authors: V. Nagarjun Yadav; S. Pazhanirajan; T. Anil Kumar

Addresses: Department of Computer Science Engineering, Annamalai University, Annamalai Nagar, Chidambaram – 608002, Tamil Nadu, India ' Department of Computer Science Engineering, Annamalai University, Annamalai Nagar, Chidambaram – 608002, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, Anurag University, Venkatapur, Ghatkesar Rd., Hyderabad, Telangana 500088, India

Abstract: The stock market (SM) is fundamentally nonlinear in nature and the people invest in SM on the basis of predictions. The SM prediction is a highly challenging and complex process. The classical techniques may not guarantee the prediction reliability. Hence, a deep long short-term memory fused recurrent neural network (DLSTMFRNN) is presented for reliable SM prediction. Here, input time series data is obtained from the database and it is pre-processed utilising missing value imputation. Thereafter, features are extracted and then feds to the feature selection phase, in which the features are selected employing the Soergel metric. Finally, SM prediction is carried out utilising DLSTMFRNN, which is a new network designed by incorporating DLSTM and RNN. The DLSTMFRNN obtained a minimal mean absolute percentage error (MAPE) of 17.65, mean squared error (MSE) of 0.116, root mean square error (RMSE) of 0.341 and relative absolute error (RAE) of 0.156.

Keywords: deep long short-term memory; DLSTM; recurrent neural network; RNN; stock market; SM; Soergel metric.

DOI: 10.1504/IJWET.2024.139850

International Journal of Web Engineering and Technology, 2024 Vol.19 No.2, pp.148 - 169

Received: 04 Sep 2023
Accepted: 08 Jan 2024
Published online: 08 Jul 2024 *

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