Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Adaptive Fuzzy C-Regression Modeling for Time Series Forecasting

Authors
Leandro Maciel, André Lemos, Rosangela Ballini, Fernando Gomide
Corresponding Author
Leandro Maciel
Available Online June 2015.
DOI
https://doi.org/10.2991/ifsa-eusflat-15.2015.129How to use a DOI?
Keywords
Adaptive clustering, fuzzy modeling, time series forecasting, stream data.
Abstract
The aim of the 2015 IFSA-EUSFLAT International Time Series Competition, Computational Intelligence in Forecasting (CIF), is to evaluate the performance of computational intelligence-based approaches to forecast time series of different nature. The participants must propose a unique consistent methodology for all time series. This paper suggests an adaptive fuzzy c-regression modeling approach (aFCR) for time series forecasting. The aFCR is a fuzzy clustering with affine prototypes modeling approach to develop fuzzy functional rule-based models. The approach uses participatory learning to adapt the model structure as it processes data as a stream of time series values. Computational experiments show that the aFCR forecaster is an effective tool to forecast time series.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Leandro Maciel
AU  - André Lemos
AU  - Rosangela Ballini
AU  - Fernando Gomide
PY  - 2015/06
DA  - 2015/06
TI  - Adaptive Fuzzy C-Regression Modeling for Time Series Forecasting
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 917
EP  - 924
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.129
DO  - https://doi.org/10.2991/ifsa-eusflat-15.2015.129
ID  - Maciel2015/06
ER  -