Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model
arXiv:1610.06486v110 citations
Originality Synthesis-oriented
AI Analysis
This work addresses forecasting challenges for time series data, but appears incremental as it builds on existing neuro-fuzzy and ANARX approaches.
The authors tackled the problem of forecasting non-stationary nonlinear time series by introducing an evolving weighted neuro-neo-fuzzy-ANARX model with learning procedures, which enables online processing of data streams.
An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. 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.