Adaptive Fuzzy C-Regression Modeling for Time Series Forecasting

DOI

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/ifsa-eusflat-15.2015.129
ID  - Maciel2015/06
ER  -