Title: A novel heuristically adaptive dual attention-based long short-term memory for intelligent stock market trend prediction model

Authors: Anuja Jana Naik; Madanant Jana Naik

Addresses: Department of Electronics and Computer Engineering, Padre Conceicao College of Engineering, Verna, Goa, India ' Department of Commerce, Goa Vidyaprasarak Mandal's Gopal Govind Poy Raiturcar College of Commerce and Economics, Farmagudi, Ponda Goa, India

Abstract: The deep learning method is designed for the stock market trend prediction through this paper. At first, stock market data are acquired from benchmark sources and are offered to the time series data formation phase. Deep convolutional temporal network (DCTN) is used here. Later, the attained features are provided to the prediction stage, and effective prediction is made by utilising adaptive dual attention-based long short-term memory (ADA-LSTM). Also, their parameters are tuned with the help of hybrid fruit fly spider monkey optimisation (HFF-SMO) by integrating fruit fly algorithm (FFO) and spider monkey algorithm (SMO) to attain an effective stock market trend prediction rate. Thus, the developed model secures effectively high accuracy rate in stock market trend prediction than existing approaches. Hence, the improved model obtained an effectively high accuracy rate in comparison with stock market trend prediction to existing approaches.

Keywords: stock market trend prediction; adaptive dual-based long-term memory; deep convolutional temporal networks; DCTNs; hybrid fruit fly monkey optimisation.

DOI: 10.1504/IJIIDS.2025.143488

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.1, pp.57 - 91

Received: 18 Nov 2022
Accepted: 15 Dec 2023
Published online: 23 Dec 2024 *