An Interpretable Belief Rule Base with Dynamic Reference Value for Stock Price Trend Forecasting

• Huimin Guan,
• Kangle Li,
• Zeyang Si,
• Wenxin Xu,
• Cuiping Yang,
• Jinsong Han,
• Hongyu Li,

Vol. 20, No. 1, January 31, 2026
10.3837/tiis.2026.01.006, Download Paper (Free):

• Stock price trend forecasting
• dynamic reference value
• belief rule base
• Interpretability
• dynamic adjustment mechanism.

Abstract

Accurately predicting stock price trends is crucial for the decision-making process in the field of financial intelligence. However, the non-stationarity and high volatility of financial data make its prediction a key challenge. The existing models generally struggle to balance prediction accuracy and model interpretability. Therefore, this study proposes an interpretable belief rule base with dynamic reference value (I-BRB-DR) framework for intelligent stock price trend forecasting. Firstly, a reference value dynamic adjustment mechanism is designed to ensure adaptability to sudden market changes by analyzing real-time market trends and autonomously updating the threshold of the belief rule base. Secondly, a set of interpretability constraint strategies are introduced in the parameter optimization procedure to ensure logical consistency and model interpretability of the model before and after optimization. Finally, the efficacy of I-BRB-DR is validated using historical stock market data in a case study. The results indicate that the model can not only accurately capture complex market patterns, but also provide clear rule-based explanations for its forecasts. This study proposes an efficient, interpretable, and adaptable forecast model, which provides a powerful tool for the field of financial intelligence and has the potential to expand to a wider range of time series prediction tasks.

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