An evolving methodology based on Neuro‐Fuzzy Takagi‐Sugeno network (NF‐TS) for distributed forecasting of univariate time series, is proposed. First, the unobservable components, or hidden patterns, are extracted from experimental data of the time series. Then, a distributed forecasting is performed separately for each component, considering an evolving NF‐TS associated with each extracted pattern. The evolving NF‐TS uses components data to adapt and adjust its structure, as the number of fuzzy rules increases or decreases according the behavior of the unobservable components. A recursive version of singular spectral analysis (SSA) technique is formulated, as one of the main contributions of this article, and it is applied to extract the components. The efficiency of proposed methodology is illustrated from results of comparison to others state‐of‐the‐art techniques for forecasting of various univariate time series.

中文翻译：

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基于不断发展的分布式神经模糊网络的时间序列智能预测

提出了一种基于神经模糊高木Sugeno网络（NF-TS）的演化方法，用于单变量时间序列的分布式预测。首先，从时间序列的实验数据中提取出不可观察的分量或隐藏模式。然后，考虑与每个提取模式相关联的不断发展的NF-TS，对每个组件分别执行分布式预测。不断发展的NF‐TS使用组件数据来适应和调整其结构，因为模糊规则的数量根据无法观察到的组件的行为而增加或减少。作为本文的主要贡献之一，提出了一种奇异谱分析（SSA）技术的递归版本，并将其应用于提取组分。