Title: Addressing stock market time series trends and volatility using optimised DE-LSTM model

Authors: Raghavendra Kumar; Pardeep Kumar; Yugal Kumar

Addresses: Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat (HP), India; Department of Information Technology, KIET Group of Institutions, Ghaziabad (UP), India ' Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat (HP), India ' Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat (HP), India

Abstract: Accurate time series prediction is the most challenging trend for research communities in the machine learning era. Stock market data is the most dynamic and volatile time series data that holds the world economy. Recent studies proposed various core and hybrid machine learning models to get accurate stock forecasting. Existing work put forward long short-term memory (LSTM) to implement sequential time series data. In this paper, a new nonlinear hybrid model is proposed using customised LSTM and differential evolution (DE) algorithms. DE brings the optimisation of selection of parameters and provides stability between complexity and learning performance of the hybrid model. The paper explores the forecasting accuracy of the stock market trends and volatility using hybrid model DE-LSTM. The proposed hybrid model obtained significant improvement as MAE, RMSE and MAPE are 0.21167, 2.48198 and 2.68331 respectively, practiced for a diversified portfolio of Bombay Stock Exchange, India (BSE30).

Keywords: stock market trends; volatility; time series data; long short-term memory; LSTM; differential evolution; DE.

DOI: 10.1504/IJOR.2024.140482

International Journal of Operational Research, 2024 Vol.50 No.4, pp.426 - 445

Received: 12 Aug 2021
Accepted: 10 Nov 2021
Published online: 20 Aug 2024 *

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