TLD-SVGP: A novel stock price prediction model based on time attention-driven LSTM network and dynamically balanced scalable sparse variational Gaussian process

Abstract

Stock price prediction remains a challenging yet critical task in financial research, with significant implications for both investors and policymakers. However, due to the combined influence of external factors (e.g., political and market conditions) and internal factors (e.g., managerial competence and organizational structure), stock prices exhibit high volatility. In prior studies, recurrent neural network (RNN)-based deep learning models have dominated, where stock price data is modeled as a time series. Most researchers have focused on single time-scale features for model training, failing to capture stock price fluctuations across multiple time scales, ultimately leading to suboptimal performance in highly volatile periods. To address this issue, we propose a time attention-driven long short-term memory (TAD-LSTM) network combined with a dynamic balanced scalable sparse variational Gaussian process (DBS-SVGP) auxiliary learning method for stock price prediction. The TAD-LSTM module extracts coupling relationships between temporal patterns, enabling efficient feature extraction and fusion. Meanwhile, the DBS-SVGP module leverages temporal and feature variables to enhance TAD-LSTM outputs, enabling precise prediction across different time-scale fluctuations while overcoming scalability limitations in large datasets. Furthermore, the proposed approach integrates symmetry principles, ensuring robust predictive performance under dynamic market conditions. We conducted extensive experiments on the CSI300 Index Dataset, Alibaba Stock Dataset, Google Stock Market Dataset, and Netflix Stock Price Dataset to evaluate our proposed approach, systematically comparing it against state-of-the-art stock price prediction models. Experimental results demonstrate that TLD-SVGP outperforms all competing models across all four datasets, achieving the highest average $R^2$ value (96.26%) and the lowest average MAPE (4.59%).