ANFIS_unfolded_in_time for multivariate time series forecasting

Date

2004-10-01

Author

Sisman-Yilmaz, Na
Alpaslan, Ferda Nur
Jain, L

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Cognitive Neuroscience, Artificial Intelligence, Computer Science Applications

URI

https://hdl.handle.net/11511/49026

Journal

NEUROCOMPUTING

DOI

https://doi.org/10.1016/j.neucom.2004.03.009

Collections

Department of Computer Engineering, Article

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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: https://hdl.handle.net/11511/49026.