Recurrent ANFIS for Time Series Forecasting
Pp. 156-164 (9)
Busenur Sarıca, Erol Egrioglu and Barıs Asıkgil
A few recurrent ANFIS approaches were proposed in the literature. Two
main types of recurrences are possible in ANFIS architecture. Feedback can be made
for input layer or right sides of Sugeno-type rules. In this study, a new type recurrent
ANFIS is proposed for forecasting. Feedback mechanism is embedded to ANFIS by
using squares of error terms as inputs in right sides of Sugeno-type fuzzy rules. The
training of the proposed ANFIS is made by using particle swarm optimization
technique. The proposed method was tested on some real world time series data and it
is compared with some alternative forecasting methods in the literature. It was shown
that the proposed method has the best forecasting performance.
ANFIS, Fuzzy C-Means, Fuzzy Inference System, Particle Swarm
Optimization, Recurrent Networks.
Department of Statistics, Faculty of Arts and Sciences, Marmara University, İstanbul, Turkey.