Associative model for the forecasting of time series based on the gamma classifier

Itzamá López-Yáñez; Leonid Sheremetov; Cornelio Yáñez-Márquez

doi:10.1007/978-3-642-38989-4_31

Associative model for the forecasting of time series based on the gamma classifier

Itzamá López-Yáñez, Leonid Sheremetov, Cornelio Yáñez-Márquez

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

3 Scopus citations

Abstract

The paper describes a novel associative model for the forecasting of time series in petroleum engineering. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. The objective is to reproduce and predict future oil production in different scenarios in an adjustable time window. The distinctive features of the experimental data set are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifier-based predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed.

Original language	English
Title of host publication	Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings
Pages	304-313
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-38989-4_31
State	Published - 2013
Event	5th Mexican Conference on Pattern Recognition, MCPR 2013 - Queretaro, Mexico Duration: 26 Jun 2013 → 29 Jun 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7914 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th Mexican Conference on Pattern Recognition, MCPR 2013
Country/Territory	Mexico
City	Queretaro
Period	26/06/13 → 29/06/13

Keywords

Gamma classifier
Time series forcasting
associative models
oil production time series

Access to Document

10.1007/978-3-642-38989-4_31

Cite this

López-Yáñez, I., Sheremetov, L., & Yáñez-Márquez, C. (2013). Associative model for the forecasting of time series based on the gamma classifier. In Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings (pp. 304-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7914 LNCS). https://doi.org/10.1007/978-3-642-38989-4_31

López-Yáñez, Itzamá ; Sheremetov, Leonid ; Yáñez-Márquez, Cornelio. / Associative model for the forecasting of time series based on the gamma classifier. Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings. 2013. pp. 304-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b3989e71e4ae40e6b4ca89068f29250b,

title = "Associative model for the forecasting of time series based on the gamma classifier",

abstract = "The paper describes a novel associative model for the forecasting of time series in petroleum engineering. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. The objective is to reproduce and predict future oil production in different scenarios in an adjustable time window. The distinctive features of the experimental data set are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifier-based predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed.",

keywords = "Gamma classifier, Time series forcasting, associative models, oil production time series",

author = "Itzam{\'a} L{\'o}pez-Y{\'a}{\~n}ez and Leonid Sheremetov and Cornelio Y{\'a}{\~n}ez-M{\'a}rquez",

year = "2013",

doi = "10.1007/978-3-642-38989-4_31",

language = "Ingl{\'e}s",

isbn = "9783642389887",

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

pages = "304--313",

booktitle = "Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings",

note = "5th Mexican Conference on Pattern Recognition, MCPR 2013 ; Conference date: 26-06-2013 Through 29-06-2013",

}

López-Yáñez, I, Sheremetov, L & Yáñez-Márquez, C 2013, Associative model for the forecasting of time series based on the gamma classifier. in Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7914 LNCS, pp. 304-313, 5th Mexican Conference on Pattern Recognition, MCPR 2013, Queretaro, Mexico, 26/06/13. https://doi.org/10.1007/978-3-642-38989-4_31

Associative model for the forecasting of time series based on the gamma classifier. / López-Yáñez, Itzamá; Sheremetov, Leonid; Yáñez-Márquez, Cornelio.
Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings. 2013. p. 304-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7914 LNCS).

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

TY - GEN

T1 - Associative model for the forecasting of time series based on the gamma classifier

AU - López-Yáñez, Itzamá

AU - Sheremetov, Leonid

AU - Yáñez-Márquez, Cornelio

PY - 2013

Y1 - 2013

N2 - The paper describes a novel associative model for the forecasting of time series in petroleum engineering. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. The objective is to reproduce and predict future oil production in different scenarios in an adjustable time window. The distinctive features of the experimental data set are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifier-based predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed.

AB - The paper describes a novel associative model for the forecasting of time series in petroleum engineering. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. The objective is to reproduce and predict future oil production in different scenarios in an adjustable time window. The distinctive features of the experimental data set are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifier-based predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed.

KW - Gamma classifier

KW - Time series forcasting

KW - associative models

KW - oil production time series

UR - http://www.scopus.com/inward/record.url?scp=84884393137&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-38989-4_31

DO - 10.1007/978-3-642-38989-4_31

M3 - Contribución a la conferencia

SN - 9783642389887

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

SP - 304

EP - 313

BT - Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings

T2 - 5th Mexican Conference on Pattern Recognition, MCPR 2013

Y2 - 26 June 2013 through 29 June 2013

ER -

López-Yáñez I, Sheremetov L, Yáñez-Márquez C. Associative model for the forecasting of time series based on the gamma classifier. In Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings. 2013. p. 304-313. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-38989-4_31

Associative model for the forecasting of time series based on the gamma classifier

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this