A Fuzzy Time Series-Based Model Using Particle Swarm Optimization and Weighted Rules
This work addresses incremental improvements in forecasting accuracy for researchers in fuzzy time series analysis.
The paper tackled limitations in high-order fuzzy time series models, such as inconsistent forecast rules and reduced data utilization, by introducing a novel model combining particle swarm optimization and weighted summation, resulting in accurate time series modeling compared to previous methods.
During the last decades, a myriad of fuzzy time series models have been proposed in scientific literature. Among the most accurate models found in fuzzy time series, the high-order ones are the most accurate. The research described in this paper tackles three potential limitations associated with the application of high-order fuzzy time series models. To begin with, the adequacy of forecast rules lacks consistency. Secondly, as the model's order increases, data utilization diminishes. Thirdly, the uniformity of forecast rules proves to be highly contingent on the chosen interval partitions. To address these likely drawbacks, we introduce a novel model based on fuzzy time series that amalgamates the principles of particle swarm optimization (PSO) and weighted summation. Our results show that our approach models accurately the time series in comparison with previous methods.