OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index.
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| Title: | OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index. |
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| Authors: | Yeasin, Md.1 (AUTHOR), Paul, Ranjit Kumar1 (AUTHOR) ranjitstat@gmail.com |
| Source: | Journal of Supercomputing. Jan2024, Vol. 80 Issue 2, p1568-1597. 30p. |
| Subjects: | Stochastic learning models, Deep learning, Standard deviations, Forecasting, Algorithms, Mathematical optimization |
| Geographic Terms: | India |
| Abstract: | Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 174801215 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yeasin%2C+Md%2E%22">Yeasin, Md.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Paul%2C+Ranjit+Kumar%22">Paul, Ranjit Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ranjitstat@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Jan2024, Vol. 80 Issue 2, p1568-1597. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Stochastic+learning+models%22">Stochastic learning models</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22India%22">India</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing 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/s11227-023-05542-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1568 Subjects: – SubjectFull: Stochastic learning models Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Standard deviations Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: India Type: general Titles: – TitleFull: OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yeasin, Md. – PersonEntity: Name: NameFull: Paul, Ranjit Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 80 – Type: issue Value: 2 Titles: – TitleFull: Journal of Supercomputing Type: main |
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