LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting

• Published in: Sci Vol. 7; no. 1; p. 7
• Main Authors: Kabir, Md R., Bhadra, Dipayan, Ridoy, Moinul, Milanova, Mariofanna
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Accuracy; Carbon; Deep learning; Digital currencies; financial time series; Forecasting; Futures; LSTM; MLP; Neural networks; Random walk theory; Securities markets; Stock exchanges; stock market; Stock prices; Support vector machines; Time series; Transformer; Trends
• ISSN: 2413-4155; 2413-4155
• DOI: 10.3390/sci7010007

The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers.