Adaptive Fuzzy C-Regression Modeling for Time Series Forecasting

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.