Models of performance of time series forecasters.
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| Title: | Models of performance of time series forecasters. |
|---|---|
| Authors: | Graff, Mario1 mgraffg@dep.fie.umich.mx, Escalante, Hugo Jair2 hugojair@inaoep.mx, Cerda-Jacobo, Jaime1 jcerda@umich.mx, Avalos Gonzalez, Alberto1 javalos@umich.mx |
| Source: | Neurocomputing. Dec2013, Vol. 122, p375-385. 11p. |
| Subjects: | Computer performance, Time series analysis, Machine learning, Problem solving, Automation, Algorithms |
| Abstract: | Abstract: One of the first steps when approaching any machine learning task is to select, among all the available procedures, which one is the most adequate to solve a particular problem; in automated problem solving this is known as the algorithm selection problem. Of course, this problem is also present in the field of time series forecasting, there, one needs to select the forecaster that makes the most accurate predictions. Generally, this selection task is manually performed by analyzing the characteristics of the time series, thus relying on the expertise that one has on the available forecasters. In this paper, we propose an automatic procedure to choose a forecaster given a set of candidates, i.e., to solve the algorithm selection problem on this domain. To do so, we follow two paths. Firstly, we propose to model the performance of the forecasters using a linear combination of features that were previously used to assess the problem difficulty of evolutionary algorithms, together with a set of features we propose in this paper. Then, this model is used to predict the performance of the forecasters and based on these predictions the forecaster is selected. Our second approach is to treat this algorithm selection process as a classification task where the descriptors of each time series are the proposed features. To show the capabilities of our approach, we test the forecasters on the time series of the M1 and M3 time series competitions and used three different forecasters. In all the cases tested, our proposals outperform the performance of the three forecasters indicating the viability of our approach. [Copyright &y& Elsevier] |
| Copyright of Neurocomputing is the property of Elsevier B.V. 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 90312172 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Models of performance of time series forecasters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Graff%2C+Mario%22">Graff, Mario</searchLink><relatesTo>1</relatesTo><i> mgraffg@dep.fie.umich.mx</i><br /><searchLink fieldCode="AR" term="%22Escalante%2C+Hugo+Jair%22">Escalante, Hugo Jair</searchLink><relatesTo>2</relatesTo><i> hugojair@inaoep.mx</i><br /><searchLink fieldCode="AR" term="%22Cerda-Jacobo%2C+Jaime%22">Cerda-Jacobo, Jaime</searchLink><relatesTo>1</relatesTo><i> jcerda@umich.mx</i><br /><searchLink fieldCode="AR" term="%22Avalos+Gonzalez%2C+Alberto%22">Avalos Gonzalez, Alberto</searchLink><relatesTo>1</relatesTo><i> javalos@umich.mx</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neurocomputing%22">Neurocomputing</searchLink>. Dec2013, Vol. 122, p375-385. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+performance%22">Computer performance</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: One of the first steps when approaching any machine learning task is to select, among all the available procedures, which one is the most adequate to solve a particular problem; in automated problem solving this is known as the algorithm selection problem. Of course, this problem is also present in the field of time series forecasting, there, one needs to select the forecaster that makes the most accurate predictions. Generally, this selection task is manually performed by analyzing the characteristics of the time series, thus relying on the expertise that one has on the available forecasters. In this paper, we propose an automatic procedure to choose a forecaster given a set of candidates, i.e., to solve the algorithm selection problem on this domain. To do so, we follow two paths. Firstly, we propose to model the performance of the forecasters using a linear combination of features that were previously used to assess the problem difficulty of evolutionary algorithms, together with a set of features we propose in this paper. Then, this model is used to predict the performance of the forecasters and based on these predictions the forecaster is selected. Our second approach is to treat this algorithm selection process as a classification task where the descriptors of each time series are the proposed features. To show the capabilities of our approach, we test the forecasters on the time series of the M1 and M3 time series competitions and used three different forecasters. In all the cases tested, our proposals outperform the performance of the three forecasters indicating the viability of our approach. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. 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: 10.1016/j.neucom.2013.05.035 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 375 Subjects: – SubjectFull: Computer performance Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Problem solving Type: general – SubjectFull: Automation Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Models of performance of time series forecasters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Graff, Mario – PersonEntity: Name: NameFull: Escalante, Hugo Jair – PersonEntity: Name: NameFull: Cerda-Jacobo, Jaime – PersonEntity: Name: NameFull: Avalos Gonzalez, Alberto IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 12 Text: Dec2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 09252312 Numbering: – Type: volume Value: 122 Titles: – TitleFull: Neurocomputing Type: main |
| ResultId | 1 |
