Advances in Time Series Forecasting

Advances in Time Series Forecasting

Volume: 2

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting ...
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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.