A Fuzzy Time Series-Based Model Using Particle Swarm Optimization and Weighted Rules

TL;DR

This paper introduces a novel fuzzy time series model that combines particle swarm optimization and weighted rules to improve accuracy and address limitations of existing high-order models.

Contribution

The paper proposes a new fuzzy time series model integrating PSO and weighted rules, enhancing forecast rule consistency and data utilization.

Findings

Model achieves higher accuracy than previous methods

Addresses limitations related to rule consistency and data utilization

Improves the robustness of high-order fuzzy time series models

Abstract

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.