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An ARIMA-based study of bibliometric index prediction
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An ARIMA-based study of bibliometric index prediction
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Journal Article
An ARIMA-based study of bibliometric index prediction
2022
Overview
Purpose>The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators.Design/methodology/approach>This paper establishes a non-stationary time series ARIMA (p, d, q) model for forecasting based on the bibliometric index data of 13 journals in the library intelligence category selected from the Chinese Social Sciences Citation Index (CSSCI) as the data source database for the period 1998–2018, and uses ACF and PACF methods for parameter estimation to predict the development trend of the bibliometric index in the next 5 years. The predicted model was also subjected to error analysis.Findings>ARIMA models are feasible for predicting bibliometric indicators. The model predicted the trend of the four bibliometric indicators in the next 5 years, in which the number of publications showed a decreasing trend and the H-value, average citations and citations showed an increasing trend. Error analysis of the model data showed that the average absolute percentage error of the four bibliometric indicators was within 5%, indicating that the model predicted well.Research limitations/implications>This study has some limitations. 13 Chinese journals were selected in the field of Library and Information Science as the research objects. However, the scope of research based on bibliometric indicators of Chinese journals is relatively small and cannot represent the evolution trend of the entire discipline. Therefore, in the future, the authors will select different fields and different sources for further research.Originality/value>This study predicts the trend changes of bibliometric indicators in the next 5 years to understand the trend of bibliometric indicators, which is beneficial for further in-depth research. At the same time, it provides a new and effective method for predicting bibliometric indicators.
