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.

该研究介绍了一种新的模型，基于模糊时间序列结合粒子群优化和加权求和原理，以解决高阶模糊时间序列模型应用中的几个潜在限制，包括预测规则的一致性、随着模型阶数增加数据利用的减少，以及预测规则的一致性与所选区间划分高度相关的问题。研究结果表明，与之前的方法相比，我们的方法能够更准确地对时间序列进行建模。

基于粒子群优化和加权规则的模糊时间序列模型