Stochastic Model and Rhythm-Adaptive Technologies of Statistical Analysis and Forecasting of Economic Processes with Cyclic Components

• Published in: Forecasting Vol. 7; no. 2; p. 20
• Main Authors: Lupenko, Serhii, Horkunenko, Andrii
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Adaptive technology; Analysis; Business cycles; Confidence intervals; cyclic economic processes; cyclic random process; Decision making; Economic forecasting; Economic growth; Economic indicators; Effectiveness; Estimation; Forecasting; Functions (mathematics); GDP; Gross Domestic Product; Learning models (Stochastic processes); mathematical modeling; Mathematical models; Polynomials; Random processes; Rhythm; rhythm-adaptive statistical processing; Statistical analysis; Stochastic models; Poland; China
• ISSN: 2571-9394; 2571-9394
• DOI: 10.3390/forecast7020020

This article presents a mathematical model of cyclical economic processes, formulated as the sum of a deterministic polynomial function and a cyclic random process that simultaneously captures trend, stochasticity, cyclicity, and rhythm variability. Building on this stochastic framework, we propose rhythm-adaptive statistical techniques for estimating the probabilistic characteristics of the cyclic component; by adjusting to rhythm changes, these techniques improve estimation accuracy. We also introduce a forecasting procedure that constructs a system of rhythm-adaptive confidence intervals for future cycles. The effectiveness of the model and associated methods is demonstrated through a series of computational experiments using Federal Reserve Economic Data. Results show that the rhythm-adaptive forecasting approach achieves mean absolute errors less than half of those produced by a comparable non-adaptive method, underscoring its practical advantage for the analysis and prediction of cyclic economic phenomena.