ANFIS_unfolded_in_time for multivariate time series forecasting

Sisman-Yilmaz N., Alpaslan F. N., Jain L.

NEUROCOMPUTING, vol.61, pp.139-168, 2004 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 61
  • Publication Date: 2004
  • Doi Number: 10.1016/j.neucom.2004.03.009
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.139-168
  • Keywords: neuro-fuzzy systerns, unfolding-in-time, backpropagation
  • Middle East Technical University Affiliated: Yes

Abstract

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. (C) 2004 Elsevier B.V. All rights reserved.