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Two time series, their meaning and some applications

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  • Rousseau, Ronald
  • Hu, Xiaojun

Abstract

Introducing and studying two types of time series, referred to as R1 and R2, we try to enrich the set of time series available for time dependent informetric studies. In a first part we focus on mathematical properties, while in a second part we check if these properties are visible in real data. This practical application uses data in the social sciences related to top Chinese universities. R1 sequences always increase over time, tending relatively fast to one, while R2 sequences have a decreasing tendency tending to zero in practical cases. They can best be used over relatively short periods of time. R1 sequences can be used to detect the rate with which cumulative data increase, while R2 sequences detect the relative rate of development.

Suggested Citation

  • Rousseau, Ronald & Hu, Xiaojun, 2013. "Two time series, their meaning and some applications," Journal of Informetrics, Elsevier, vol. 7(3), pages 603-610.
  • Handle: RePEc:eee:infome:v:7:y:2013:i:3:p:603-610
    DOI: 10.1016/j.joi.2013.03.006
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    References listed on IDEAS

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    1. Ismael Rafols & Martin Meyer, 2010. "Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 263-287, February.
    2. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    3. Ye, Fred Y. & Rousseau, Ronald, 2008. "The power law model and total career h-index sequences," Journal of Informetrics, Elsevier, vol. 2(4), pages 288-297.
    4. Vanclay, Jerome K., 2012. "Publication patterns of award-winning forest scientists and implications for the Australian ERA journal ranking," Journal of Informetrics, Elsevier, vol. 6(1), pages 19-26.
    5. Bettencourt, Luís M.A. & Kaiser, David I. & Kaur, Jasleen, 2009. "Scientific discovery and topological transitions in collaboration networks," Journal of Informetrics, Elsevier, vol. 3(3), pages 210-221.
    6. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    7. Van Looy, Bart & Callaert, Julie & Debackere, Koenraad, 2006. "Publication and patent behavior of academic researchers: Conflicting, reinforcing or merely co-existing?," Research Policy, Elsevier, vol. 35(4), pages 596-608, May.
    8. Hu, Xiaojun & Rousseau, Ronald & Chen, Jin, 2011. "Time series of outgrow indices," Journal of Informetrics, Elsevier, vol. 5(3), pages 413-421.
    9. Liu, Yuxian & Rousseau, Ronald, 2008. "Definitions of time series in citation analysis with special attention to the h-index," Journal of Informetrics, Elsevier, vol. 2(3), pages 202-210.
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