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Integration of Intuitionistic Fuzzy Logic and Modified Ordinary Differential Equations for Robust Financial Market Forecasting
Velichka Traneva, Slavi Georgiev, Stoyan Tranev, Venelin Todorov and Ivan Georgiev
In financial market analysis, large datasets are commonly used to study indicator prices and predict price movements. Managing such datasets requires advanced forecasting strategies that address stochastic and differential equations. This study introduces a novel price forecasting approach by integrating modified ordinary differential equations (MODEs) with intuitionistic fuzzy sets (IFSs). The ODEs method employs periodic and polynomial function forms, with coefficients determined using the Weighted Least Squares Method. By exploring various parameter configurations over selected time horizons, the model adapts to diverse data dynamics. Intuitionistic fuzzy logic combines the “votes” of model instances, enhancing prediction resilience and accuracy. Our results show significant improvements over traditional techniques, demonstrating suitability for handling high uncertainty and incomplete information through flexible forecasting.
Keywords: Index matrices, intuitionistic fuzzy logic, modified ordinary differential equations, price forecasting