Intelligent forecasting of time series based on evolving distributed Neuro‐Fuzzy network

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...

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Bibliographic Details
Published inComputational intelligence Vol. 36; no. 3; pp. 1394 - 1413
Main Authors Rodrigues Júnior, Selmo Eduardo, Oliveira Serra, Ginalber Luiz
Format Journal Article
LanguageEnglish
Published Hoboken Blackwell Publishing Ltd 01.08.2020
Subjects
distributed Neuro‐Fuzzy Takagi‐Sugeno
evolving systems
Forecasting
hidden patterns
Spectrum analysis
Time series
time series forecasting
unobservable components
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Summary: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.
ISSN:0824-7935
1467-8640
DOI:10.1111/coin.12383