An enhanced fuzzy time series forecasting model using Gaussian membership functions and PSO-based parameter optimization
DOI:
https://doi.org/10.54939/1859-1043.j.mst.CSCE9.2025.23-34Keywords:
Fuzzy time series; Fuzzy logical relationship groups; Gaussian functions; PSO; Enrollments; Ron95.Abstract
In the context of data-driven decision making and increasing market volatility, accurate time series forecasting plays a crucial role in sectors such as education and energy. This paper presents a novel fuzzy time series (FTS) model that integrates Gaussian membership functions with Particle Swarm Optimization (PSO) to simultaneously optimize the universe of discourse ( , ) and the standard deviations of Gaussian functions. The key innovation lies in the combination of dynamic fuzzification with PSO-based parameter optimization, effectively addressing the limitations of static partitioning and manual tuning in traditional FTS models. Furthermore, the model employs time-dependent fuzzy logical relationship groups (TD-FLRGs) to improve forecasting accuracy in handling non-linear and uncertain data. The proposed model is evaluated on the Alabama university enrollment dataset (1971–1992) and validated using Vietnam's RON95 gasoline prices. With seven intervals and first-order relationships, it achieves a Root Mean Squared Error (RMSE) of 318.7. These results highlight the model's superior performance and demonstrate its potential as a scalable and adaptable solution for real-time forecasting in dynamic environments.
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