Time series forecasting with genetic programming.
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| Title: | Time series forecasting with genetic programming. |
|---|---|
| Authors: | Graff, Mario1, Tellez, Eric1, Escalante, Hugo2, Ornelas-Tellez, Fernando3 |
| Source: | Natural Computing. Mar2017, Vol. 16 Issue 1, p165-174. 10p. |
| Subjects: | Generic programming (Computer science), Time series analysis, Artificial neural networks, Back propagation, Algorithms |
| Abstract: | Genetic programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard world problems. There has been a lot of interest in using GP to tackle forecasting problems. Unfortunately, it is not clear whether GP can outperform traditional forecasting techniques such as auto-regressive models. In this contribution, we present a comparison between standard GP systems qand auto-regressive integrated moving average model and exponential smoothing. This comparison points out particular configurations of GP that are competitive against these forecasting techniques. In addition to this, we propose a novel technique to select a forecaster from a collection of predictions made by different GP systems. The result shows that this selection scheme is competitive with traditional forecasting techniques, and, in a number of cases it is statistically better. [ABSTRACT FROM AUTHOR] |
| Copyright of Natural Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 121264463 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Time series forecasting with genetic programming. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Graff%2C+Mario%22">Graff, Mario</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tellez%2C+Eric%22">Tellez, Eric</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Escalante%2C+Hugo%22">Escalante, Hugo</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ornelas-Tellez%2C+Fernando%22">Ornelas-Tellez, Fernando</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Natural+Computing%22">Natural Computing</searchLink>. Mar2017, Vol. 16 Issue 1, p165-174. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Generic+programming+%28Computer+science%29%22">Generic programming (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Genetic programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard world problems. There has been a lot of interest in using GP to tackle forecasting problems. Unfortunately, it is not clear whether GP can outperform traditional forecasting techniques such as auto-regressive models. In this contribution, we present a comparison between standard GP systems qand auto-regressive integrated moving average model and exponential smoothing. This comparison points out particular configurations of GP that are competitive against these forecasting techniques. In addition to this, we propose a novel technique to select a forecaster from a collection of predictions made by different GP systems. The result shows that this selection scheme is competitive with traditional forecasting techniques, and, in a number of cases it is statistically better. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Natural Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11047-015-9536-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 165 Subjects: – SubjectFull: Generic programming (Computer science) Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Back propagation Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Time series forecasting with genetic programming. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Graff, Mario – PersonEntity: Name: NameFull: Tellez, Eric – PersonEntity: Name: NameFull: Escalante, Hugo – PersonEntity: Name: NameFull: Ornelas-Tellez, Fernando IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 15677818 Numbering: – Type: volume Value: 16 – Type: issue Value: 1 Titles: – TitleFull: Natural Computing Type: main |
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