Analyzing Citation Dynamics and Forecasting Academic Trends: A Multi-Criteria Decision-Making Approach Using ARIMA and AHP

Abstract

and employs forecasting models to predict future publication trends. Citation dynamics are critical in bibliometric studies because they influence research visibility and academic recognition. Using a dataset of 505 academic publications from Scopus, this study applies correlation analysis, time-series forecasting (ARIMA), and multi-criteria decision-making (MCDM) techniques, particularly the Analytic Hierarchy Process (AHP). Pearson’s correlation analysis revealed a weak negative correlation ((r = -0.154)) between publication year and citation count, indicating that older publications tended to have more citations, but other factors significantly influenced citation accumulation. Time-series forecasting using ARIMA (1,1,1) and Holt’s Exponential Smoothing Model evaluated future publication trends, with ARIMA demonstrating higher short-term accuracy. The AHP model ranks research papers based on citation count, publication year, and relevance to MCDM, assigning higher weights to the citation count (63.3%)). This study highlights the evolving role of artificial intelligence and big data analytics in bibliometric analysis and underscores the need for hybrid models that integrate machine learning techniques with traditional decision-support frameworks. Future research should incorporate journal impact factors, author reputation, and open-access status to refine the citation prediction models. The findings contribute to a data-driven understanding of academic influence, providing insights for researchers, policymakers, and institutions aiming to enhance research visibility and scholarly impact.