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Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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Estimation of Scores and Prediction of Ranks - A Study Based on NIRF Ranks of Engineering Colleges

Authors: Ashwini K; Sumathi Prabhu;

Estimation of Scores and Prediction of Ranks - A Study Based on NIRF Ranks of Engineering Colleges

Abstract

National Institutional Ranking Framework (NIRF) is a ranking methodology to assess the performance of the institutions of higher education in India. The scores and rankings of reputed institutes in India are released every year since 2016. The present paper predicts the scores for ranks for future years using time series approach. Regression models using linear and quadratic trend components are built for selected rankings. The models are assessed using the coefficient of determination and forecast accuracy measures. It is observed that the predicted scores are very close to the original scores. The official results of NIRF for 2024 and 2025 support the findings. Based on this, scores for 2026 are also estimated.

Keywords

Engineering rankings, linear trend, NIRF, time series analysis.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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