ANFIS_unfolded_in_time for multivariate time series forecasting

Sisman-Yilmaz, Na
Alpaslan, Ferda Nur
Jain, L
This paper proposes a temporal neuro-fuzzy system named ANFIS_unfolded_in_time which is designed to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data by using fuzzy rules. It is a modification of ANFIS neuro-fuzzy model. The rule base of ANFIS_unfolded_in_time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, back-propagation learning algorithm is used. The system takes the multivariate data and the number of lags needed to construct the unfolded model in order to describe a variable and predicts the future behavior. Computer simulations are performed by using real multivariate data and a benchmark problem (Gas Furnace Data). Experimental results show that the proposed model achieves online learning and prediction on temporal data. The results are compared with the results of ANFIS.


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Citation Formats
N. Sisman-Yilmaz, F. N. Alpaslan, and L. Jain, “ANFIS_unfolded_in_time for multivariate time series forecasting,” NEUROCOMPUTING, pp. 139–168, 2004, Accessed: 00, 2020. [Online]. Available: