Abstract
The aim of this paper is to simultaneously identify and estimate a non-linear autoregressive time series using a flexible neuro-fuzzy model. We provide a self organization and incremental mechanism to the adaptation process of the neuro-fuzzy model. The self organization mechanism searches for a suitable set of premises and consequents to enhance the time series estimation performance, while the incremental method selects influential lags in the model description.
Experimental results indicate that our proposal reliably identifies appropriate lags for non-linear time series. Our proposal is illustrated by simulations on both synthetic and real data.
This work was supported in part by the Fondecyt 1070220 and DGIP-UTFSM research grants. The work of C. Moraga was partially supported by the Foundation for the Advancement of Soft Computing, Mieres, Asturias, Spain. E-mail addresses: avelozb@inf.utfsm.cl (A. Veloz), vector@inf.utfsm.cl (H. Allende-Cid), hallende@inf.utfsm.cl (H. Allende), mail@claudio-moraga.eu (C. Moraga) and rodrigo.salas@uv.cl (R. Salas)
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Veloz, A., Allende-Cid, H., Allende, H., Moraga, C., Salas, R. (2009). A Flexible Neuro-Fuzzy Autoregressive Technique for Non-linear Time Series Forecasting. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04595-0_3
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DOI: https://doi.org/10.1007/978-3-642-04595-0_3
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