Abstract:
This article improves the forecasting toolkit for the Consumer Price Index under conditions of macroeconomic uncertainty. The study proposes a hybrid approach based on cross-verification of heterogeneous data and fuzzy logic. The
mathematical core of the model utilizes the Bellman-Zadeh multiplicative synthesis scheme, which eliminates the risk
compensation effect and enhances sensitivity to hidden economic shocks. The proposed methodology successfully handles
nonlinear volatility and reduces data asymmetry across various international and national statistical sources. Research
results confirm the model's high reliability in identifying «grey zones» and transitional inflationary phases, thereby enabling a shift from reactive monitoring to proactive adaptation through a possibility degree for 2025-2026 economic scenarios within a scalable framework adaptable to other interconnected socio-economic systems
