Abstract
ABSTRACT Forecasting future citations based on trends in A.I. publications in libraries is essential for understanding the long-term impact of academic research. This study evaluates how well historical citation data from 2003 to 2023 can predict future citation patterns and compares the effectiveness of various forecasting models. The analysis includes traditional time series models like ARIMA and Exponential Smoothing, as well as machine learning techniques such as Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. ARIMA and Exponential Smoothing are chosen for their robustness in handling historical trends, while machine learning models are applied to capture complex, nonlinear relationships. The study assesses model performance using metrics such as Mean Squared Error (MSE) and R-squared values, identifying the most accurate models for forecasting. The findings reveal that Exponential Smoothing provides reliable forecasts of future citations, while Random Forest Regression demonstrates superior performance among machine learning techniques. Additionally, linear regression models predict citation and publication trends effectively, though they may benefit from more sophisticated approaches for greater accuracy. This research offers valuable insights for researchers, institutions, and policymakers, helping them anticipate the future impact of academic publications and guiding strategic decisions in research and funding.
Published Version
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