Publication2026
An Informer-Based Real-Time Forecasting with Inverse Scaling Optimization and Bayesian Hyper-parameter Tuning
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Procedia Computer Science
- Volume
- 283
- Pages
- 5080-5089
- Published
- 1 January 2026
- DOI
- 10.1016/j.procs.2026.06.561
- Scopus ID
- 105046104915
- ISSN
- 18770509
- Authors
- 3
- Open access
- Yes
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Research applies Informer-based deep learning with Bayesian optimization for real-time financial forecasting, demonstrating advanced research capacity in transformer architectures and hyperparameter tuning methodologies.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Application of digital technology and artificial intelligence in financial prediction systems showcases innovation in machine learning algorithm development and deployment.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “An Informer-Based Real-Time Forecasting with Inverse Scaling Optimization and Bayesian Hyper-parameter Tuning”. Kortex.