Stochastic analysis of citation time series of emergent research topics

Detecting and forecasting emerging research topics has become more demanded by researchers and practitioners. Bibliometrics provide a promising way to detect emerging research topics at an early stage. However, reliably forecasting the emergence of a research topic still remains a challenge. Based on the number of cited references per year of a current research topic, we used the relative knowledge growth described as time series. The time series were analyzed stochastically. As they reveal a common pattern of memory, this memory can be used to shift the relative growth factor to the future using stochastic ARMA models. An approach to forecast the emergence of a research topic using ARMA models and thus detecting emergent research topics even earlier is proposed.