A new hybrid approach based on machine learning for more efficient time series forecasting

Hassan Bousnguar, Lotfi Najdi and Amal Battou

Data and Metadata, 2025, vol. 4, 589

Abstract: Introduction: Forecasting new student enrollment in bachelor's degree programs has emerged as a critical need for higher education institutions. Accurate enrollment predictions are essential for improving the student-teacher ratio and optimizing resource allocation. Methods: A hybrid approach combining statistical and machine learning techniques was proposed to develop accurate forecasting models. The study utilized the historical enrollment database of Ibn Zohr University, which included data from over twenty institutions dating back to 2003. This dataset was used to train and validate the proposed models. Results: The hybrid approach demonstrated superior accuracy compared to standalone statistical and machine learning models. The results indicated that the proposed method effectively captured enrollment trends and provided reliable forecasts. Conclusions: The study concluded that the hybrid approach offers a robust solution for enrollment forecasting in higher education. It highlighted the potential of combining statistical and machine learning techniques to improve prediction accuracy, thereby aiding institutions in better planning and resource management..

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:589:id:1056294dm2025589

DOI: 10.56294/dm2025589

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().