A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting

Alejandro Veloz; Héctor Allende-Cid; Héctor Allende; Claudio Moraga; Rodrigo Salas

doi:10.1007/978-3-642-04595-0_3

A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting

Alejandro Veloz, Héctor Allende-Cid, Héctor Allende, Claudio Moraga, Rodrigo Salas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings
Pages	22-29
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-04595-0_3
State	Published - 2009
Externally published	Yes
Event	13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009 - Santiago, Chile Duration: 28 Sep 2009 → 30 Sep 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5711 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009
Country/Territory	Chile
City	Santiago
Period	28/09/09 → 30/09/09

Keywords

Flexible and Incremental learning
Neuro-fuzzy models
Non-linear Autoregressive Time Series

Access to Document

10.1007/978-3-642-04595-0_3

Cite this

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 Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings (PART 1 ed., pp. 22-29). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5711 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-04595-0_3

Veloz, Alejandro ; Allende-Cid, Héctor ; Allende, Héctor et al. / A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting. Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings. PART 1. ed. 2009. pp. 22-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{771a8d845c80493aab53bb3a06af7748,

title = "A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting",

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.",

keywords = "Flexible and Incremental learning, Neuro-fuzzy models, Non-linear Autoregressive Time Series",

author = "Alejandro Veloz and H{\'e}ctor Allende-Cid and H{\'e}ctor Allende and Claudio Moraga and Rodrigo Salas",

note = "Funding Information: 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); 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009 ; Conference date: 28-09-2009 Through 30-09-2009",

year = "2009",

doi = "10.1007/978-3-642-04595-0_3",

language = "English",

isbn = "3642045944",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "22--29",

booktitle = "Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings",

edition = "PART 1",

}

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 Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5711 LNAI, pp. 22-29, 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009, Santiago, Chile, 28/09/09. https://doi.org/10.1007/978-3-642-04595-0_3

A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting. / Veloz, Alejandro; Allende-Cid, Héctor; Allende, Héctor et al.
Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings. PART 1. ed. 2009. p. 22-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5711 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting

AU - Veloz, Alejandro

AU - Allende-Cid, Héctor

AU - Allende, Héctor

AU - Moraga, Claudio

AU - Salas, Rodrigo

N1 - Funding Information: 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)

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Flexible and Incremental learning

KW - Neuro-fuzzy models

KW - Non-linear Autoregressive Time Series

UR - http://www.scopus.com/inward/record.url?scp=70849122168&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-04595-0_3

DO - 10.1007/978-3-642-04595-0_3

M3 - Conference contribution

AN - SCOPUS:70849122168

SN - 3642045944

SN - 9783642045943

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 22

EP - 29

BT - Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings

T2 - 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009

Y2 - 28 September 2009 through 30 September 2009

ER -

Veloz A, Allende-Cid H, Allende H, Moraga C, Salas R. A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting. In Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings. PART 1 ed. 2009. p. 22-29. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-04595-0_3

A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this