Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model | Zendy

Zhengbing Hu | Zendy; Yevgeniy Bodyanskiy | Zendy; Oleksii K. Tyshchenko | Zendy; Olena O. Boiko | Zendy

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Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model

Author(s) -

Zhengbing Hu,

Yevgeniy Bodyanskiy,

Oleksii K. Tyshchenko,

Olena O. Boiko

Publication year - 2016

Publication title -

international journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2074-9015

pISSN - 2074-9007

DOI - 10.5815/ijitcs.2016.10.01

Subject(s) - computer science , series (stratigraphy) , fuzzy logic , time series , nonlinear system , process (computing) , artificial intelligence , neuro fuzzy , data mining , data stream mining , machine learning , fuzzy control system , paleontology , physics , quantum mechanics , biology , operating system

An evolving weighted neuro-neo-fuzzy- ANARX model and its learn ing procedures are introduced in the article. This system is basically used for time series forecasting. It's based on neo-fuzzy elements. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.

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