Adaptive Fuzzy C-Regression Modeling for Time Series Forecasting
Leandro Maciel, André Lemos, Rosangela Ballini, Fernando Gomide
Available Online June 2015.
- https://doi.org/10.2991/ifsa-eusflat-15.2015.129How to use a DOI?
- Adaptive clustering, fuzzy modeling, time series forecasting, stream data.
- 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.
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 -