Forecasting horizon Dst index based on solar wind data using deep learning and generative artificial intelligence.
Saved in:
| Title: | Forecasting horizon Dst index based on solar wind data using deep learning and generative artificial intelligence. |
|---|---|
| Authors: | Wihayati1 (AUTHOR) wihayati@gmail.com, Purnomo, Hindriyanto Dwi1 (AUTHOR), Trihandaru, Suryasatriya2 (AUTHOR) |
| Source: | Journal of Astrophysics & Astronomy. 7/8/2026, Vol. 47 Issue 2, p1-22. 22p. |
| Subjects: | Transformer models, Generative artificial intelligence, Solar wind, Magnetic storms, Space environment, Deep learning, Generative adversarial networks, Forecasting |
| Abstract: | This paper compares Transformer architectures (Bisection Transformer, Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Approach (RoBERTa)), Generative Adversarial Networks (GAN), deep learning (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)), and Nonlinear AutoRegressive Moving Average with eXogenous (NARMAX) in forecasting the Disturbance storm-time (Dst) index using solar wind parameters. The purpose of this paper is to emphasize the importance of selecting a specific model for the 5- to 100-step-ahead forecasting horizon across various algorithmic approaches in operational space weather forecasting and geomagnetic storm prediction. In these tests, NARMAX has the lowest Root Mean Square Error (RMSE) of 14.723 at a horizon of 5 for short-term forecasting, but this decreases at longer horizons. In contrast, GAN exhibits consistent performance with minimal decline, increasing RMSE by 7.5% from horizons 5 to 100. Meanwhile, RoBERTa outperforms in the medium-term, achieving its best performance between horizons 20 and 50. Statistical analysis shows that the Transformer architecture outperforms deep learning at longer time horizons. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Astrophysics & Astronomy 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 195184225 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Forecasting horizon Dst index based on solar wind data using deep learning and generative artificial intelligence. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wihayati%22">Wihayati</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wihayati@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Purnomo%2C+Hindriyanto+Dwi%22">Purnomo, Hindriyanto Dwi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Trihandaru%2C+Suryasatriya%22">Trihandaru, Suryasatriya</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Astrophysics+%26+Astronomy%22">Journal of Astrophysics & Astronomy</searchLink>. 7/8/2026, Vol. 47 Issue 2, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+wind%22">Solar wind</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+storms%22">Magnetic storms</searchLink><br /><searchLink fieldCode="DE" term="%22Space+environment%22">Space environment</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper compares Transformer architectures (Bisection Transformer, Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Approach (RoBERTa)), Generative Adversarial Networks (GAN), deep learning (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)), and Nonlinear AutoRegressive Moving Average with eXogenous (NARMAX) in forecasting the Disturbance storm-time (Dst) index using solar wind parameters. The purpose of this paper is to emphasize the importance of selecting a specific model for the 5- to 100-step-ahead forecasting horizon across various algorithmic approaches in operational space weather forecasting and geomagnetic storm prediction. In these tests, NARMAX has the lowest Root Mean Square Error (RMSE) of 14.723 at a horizon of 5 for short-term forecasting, but this decreases at longer horizons. In contrast, GAN exhibits consistent performance with minimal decline, increasing RMSE by 7.5% from horizons 5 to 100. Meanwhile, RoBERTa outperforms in the medium-term, achieving its best performance between horizons 20 and 50. Statistical analysis shows that the Transformer architecture outperforms deep learning at longer time horizons. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Astrophysics & Astronomy 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195184225 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12036-026-10158-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Transformer models Type: general – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Solar wind Type: general – SubjectFull: Magnetic storms Type: general – SubjectFull: Space environment Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Forecasting Type: general Titles: – TitleFull: Forecasting horizon Dst index based on solar wind data using deep learning and generative artificial intelligence. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wihayati – PersonEntity: Name: NameFull: Purnomo, Hindriyanto Dwi – PersonEntity: Name: NameFull: Trihandaru, Suryasatriya IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 07 Text: 7/8/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02506335 Numbering: – Type: volume Value: 47 – Type: issue Value: 2 Titles: – TitleFull: Journal of Astrophysics & Astronomy Type: main |
| ResultId | 1 |
