Time series forecasting by means of SOM aided Fuzzy Inference Systems

Daniel Zurita; Jesús A. Carino; Enric Sala; Miguel Delgado-Prieto; Juan A. Ortega

doi:10.1109/ICIT.2015.7125354

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Daniel Zurita
, Jesús A. Carino
, Enric Sala
, Miguel Delgado-Prieto
, Juan A. Ortega

Polytechnic University of Catalonia

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

2 Citations (Scopus)

Abstract

The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

Original language	English
Title of host publication	2015 IEEE International Conference on Industrial Technology, ICIT 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1772-1778
Number of pages	7
Edition	June
ISBN (Electronic)	9781479978007
DOIs	https://doi.org/10.1109/ICIT.2015.7125354
Publication status	Published - 16 Jun 2015
Externally published	Yes
Event	2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Spain Duration: 17 Mar 2015 → 19 Mar 2015

Publication series

Name	Proceedings of the IEEE International Conference on Industrial Technology
Number	June
Volume	2015-June

Conference

Conference	2015 IEEE International Conference on Industrial Technology, ICIT 2015
Country/Territory	Spain
City	Seville
Period	17/03/15 → 19/03/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Artificial intelligence
Condition monitoring
Fuzzy neural networks
Machine learning
Predictive models
Prognosis
Time series analysis

Access to Document

10.1109/ICIT.2015.7125354

Cite this

Zurita, D., Carino, J. A., Sala, E., Delgado-Prieto, M., & Ortega, J. A. (2015). Time series forecasting by means of SOM aided Fuzzy Inference Systems. In 2015 IEEE International Conference on Industrial Technology, ICIT 2015 (June ed., pp. 1772-1778). Article 7125354 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2015-June, No. June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2015.7125354

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title = "Time series forecasting by means of SOM aided Fuzzy Inference Systems",

abstract = "The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.",

keywords = "Artificial intelligence, Condition monitoring, Fuzzy neural networks, Machine learning, Predictive models, Prognosis, Time series analysis",

author = "Daniel Zurita and Carino, \{Jes{\'u}s A.\} and Enric Sala and Miguel Delgado-Prieto and Ortega, \{Juan A.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Conference on Industrial Technology, ICIT 2015 ; Conference date: 17-03-2015 Through 19-03-2015",

year = "2015",

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day = "16",

doi = "10.1109/ICIT.2015.7125354",

language = "English",

series = "Proceedings of the IEEE International Conference on Industrial Technology",

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booktitle = "2015 IEEE International Conference on Industrial Technology, ICIT 2015",

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}

Zurita, D, Carino, JA, Sala, E, Delgado-Prieto, M & Ortega, JA 2015, Time series forecasting by means of SOM aided Fuzzy Inference Systems. in 2015 IEEE International Conference on Industrial Technology, ICIT 2015. June edn, 7125354, Proceedings of the IEEE International Conference on Industrial Technology, no. June, vol. 2015-June, Institute of Electrical and Electronics Engineers Inc., pp. 1772-1778, 2015 IEEE International Conference on Industrial Technology, ICIT 2015, Seville, Spain, 17/03/15. https://doi.org/10.1109/ICIT.2015.7125354

Time series forecasting by means of SOM aided Fuzzy Inference Systems. / Zurita, Daniel; Carino, Jesús A.; Sala, Enric et al.
2015 IEEE International Conference on Industrial Technology, ICIT 2015. June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1772-1778 7125354 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2015-June, No. June).

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

TY - GEN

T1 - Time series forecasting by means of SOM aided Fuzzy Inference Systems

AU - Zurita, Daniel

AU - Carino, Jesús A.

AU - Sala, Enric

AU - Delgado-Prieto, Miguel

AU - Ortega, Juan A.

PY - 2015/6/16

Y1 - 2015/6/16

N2 - The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

AB - The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

KW - Artificial intelligence

KW - Condition monitoring

KW - Fuzzy neural networks

KW - Machine learning

KW - Predictive models

KW - Prognosis

KW - Time series analysis

UR - https://www.scopus.com/pages/publications/84937696196

U2 - 10.1109/ICIT.2015.7125354

DO - 10.1109/ICIT.2015.7125354

M3 - Conference contribution

AN - SCOPUS:84937696196

T3 - Proceedings of the IEEE International Conference on Industrial Technology

SP - 1772

EP - 1778

BT - 2015 IEEE International Conference on Industrial Technology, ICIT 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 IEEE International Conference on Industrial Technology, ICIT 2015

Y2 - 17 March 2015 through 19 March 2015

ER -

Zurita D, Carino JA, Sala E, Delgado-Prieto M, Ortega JA. Time series forecasting by means of SOM aided Fuzzy Inference Systems. In 2015 IEEE International Conference on Industrial Technology, ICIT 2015. June ed. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1772-1778. 7125354. (Proceedings of the IEEE International Conference on Industrial Technology; June). doi: 10.1109/ICIT.2015.7125354

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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