Stochastic analysis of citation time series of emergent research topics

Leiden Repository

Stochastic analysis of citation time series of emergent research topics

Type: Article in monograph or in proceedings
Title: Stochastic analysis of citation time series of emergent research topics
Author: Förster M.Stelzer B.Schiebel E.
Journal Title: STI 2018 Conference Proceedings
Start Page: 1279
End Page: 1291
Publisher: Centre for Science and Technology Studies (CWTS)
Issue Date: 2018-09-11
Keywords: Scientometrics
Abstract: Detecting and forecasting emerging research topics has become more demanded by researchers and practitioners. Biblimetrics 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.
Handle: http://hdl.handle.net/1887/65236
 

Files in this item

Description Size View
application/pdf STI2018_paper_211.pdf 1.171Mb View/Open

This item appears in the following Collection(s)