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This paper proposes an evolving methodology based on Neuro-Fuzzy Takagi-Sugeno networks (NF-TS) distributed for time series forecasting. This proposed approach extracts the unobservable components from time series. Then, the forecast is performed separately for each component, and there is an NF-TS associated with each of these patterns, a fact that highlights the distributed characteristics of proposed approach. The NF-TS are still evolving, i.e., using the data of components to adapt and adjust their structure, where the amount of fuzzy rules of that network can increase or be reduced according to the behavior of components. The method used to extract the components is based on Singular Spectral Analysis technique. The proposed methodology was evaluated and compared with other similar techniques applied for series forecasting, presenting promising results. Resumo: Esse trabalho propõe uma metodologia evolutiva baseada em redes Neuro-Fuzzy Takagi-Sugeno (NF-TS) distribúıdas para a previsão de séries temporais. Essa abordagem proposta extrai as componentes não-observáveis a partir de uma série temporal. Em seguida, a previsão de cada componente separadamente é realizada, sendo que há uma NF-TS associada a cada um desses padrões, fato que destaca a caracteŕıstica distribúıda da proposta. As NF-TS são ainda evolutivas, ou seja, usam os dados dessas componentes para adaptar e ajustar sua estrutura, sendo que a quantidade de regras fuzzy dessa rede pode aumentar ou ser reduzida conforme o comportamento das componentes. O método utilizado para extrair as componentes é baseado na técnica de Análise Espectral Singular. A metodologia proposta foi avaliada e comparada com outras técnicas semelhantes aplicadas para a previsão de séries, apresentando resultados promissores.

Previsão Inteligente de Séries Temporais Baseada em Redes Neuro-Fuzzy Takagi-Sugeno Evolutivas Distribuídas