ArticleScientific reports2026
A fuzzy time-series driven ensemble approach for accurate forecasting of higher education rankings.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The global education system comprises many technical and non-technical institutions. The selection of an institute plays a very important role in shaping the career of a student. With such a massive number of choices out there, the decision of which institution to go to will be a huge challenge for parents as well as students. Unexpected events such as the Covid-19 pandemic even disrupted global higher education highlighting infrastructural gaps and pedagogical limitations in knowledge delivery through sudden transitions to remote learning. Institutions were financially unstable with reductions in enrolment especially of international students. Increased operations cost for digital infrastructure and health protocols also took a toll on the academia, and it became important to predict the position of the institutions effectively. Our research proposes a fuzzy time series based ensemble model, ensemble based time series association (EBTsA) for dynamically predicting institutional rankings in the highly uncertain academic environment. The model integrates a fuzzy time series and ensemble machine learning algorithm for institutional rank prediction and capturing inherent variations induced by ranking uncertainties. It uses the method of fuzzification to adaptively consider the importance of rankings in a changing way over time, both before and after the pre- and post-COVID changes. This vital rank gap in earlier studies has personified rankings as static or uniform. Various algorithms such as FTS, FCA, IFS, IFS_New and the proposed algorithm (EBTsA) are compared based on their performance in the dynamic ranking prediction. The EBTsA model quantifies ranking uncertainties and forecasts institutional ranks with a mean absolute percentage error (MAPE) of 7.12, a mean absolute scaled error (MASE) of 0.32, and a directional accuracy (DA) of 82.2, outperforming conventional deterministic models. The predictive performance of the model ensures highly accurate and reliable dynamic rank forecasts, enabling stakeholders to make informed decisions about educational institutions. Our study may contribute to two sustainable development goals (SDGs)of the United Nations Organisation (UNO), such as (SDG 4), which provides "quality education and its connection to inequality", and (SDG 10) for "reduced inequalities and its connection to education".
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