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.

翻译：在过去几十年中，科学文献中提出了大量模糊时间序列模型。在模糊时间序列中，高阶模型是精度最高的模型之一。本文所描述的研究针对高阶模糊时间序列模型应用中存在的三个潜在局限性展开探讨：首先，预测规则的充分性缺乏一致性；其次，随着模型阶数的增加，数据利用率逐渐降低；第三，预测规则的统一性高度依赖于所选区间划分。为解决这些可能存在的缺陷，我们提出了一种融合粒子群优化（PSO）原理与加权求和的模糊时间序列新模型。结果表明，与以往方法相比，所提模型能够更精确地对时间序列进行建模。