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| Integration Forecast of Journal h-index |
| Song Yanhui1,2, Fu Qiyuan1, Qiu Junping2 |
1.School of Management, Hangzhou Dianzi University, Hangzhou 310018
2.China Academy of Science and Education Evaluation, Hangzhou Dianzi University, Hangzhou 310018 |
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Abstract The prediction of academic influence of journals has gradually attracted extensive attention in journal and academic circles. Hirsch pointed out that h-index has better predictive ability compared with other bibliometric indicators. Predicting the development of journal h-index is equivalent to predicting the evolution of journal impact. On the basis of the Chinese Social Science Citation Index (CSSCI) and 13 core journals of library and information science in China, the time series prediction models of Vector Autoregression (VAR), Vector Error Correction (VEC), and Long Short-Term Memory (LSTM) are established to dynamically predict the future h-index of journals. Then, on the basis of the integrated forecast method, the integrated forecast values of the above three models are formed, and the precision of each model and method is compared. Empirical results show that the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of the integrated forecast method are lower than those of the three single prediction models, thereby improving prediction stability. The journal h-index shows a steady growth trend in the future, and the academic influence of the journal in the field of library and information will develop positively.
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Received: 27 April 2022
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