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Modelling Circular Time Series with Applications

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Directional Statistics for Innovative Applications

Part of the book series: Forum for Interdisciplinary Mathematics ((FFIM))

Abstract

Circular time series modelling has posed a big challenge to many researchers due to the nature of observations. This article traces different circular time series models and the methods adopted in the development. It goes further to illustrate the use of these methods and the resultant circular time series models of wind direction data. The application involved hourly data on wind direction for the two major seasons (wet and dry seasons) in Nigeria. Training and development of the models are done using R codes and SPSS version 24. The results shows that score-driven models based on von Misses distribution is the best model using some error statistics and goodness of fit tests.

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Nigeria, Nsukka, Nigeria

    Fidelis Ifeanyi Ugwuowo

  2. Department of Mathematics and Statistics, School of Applied Science and Technology, Federal Polytechnic, Offa, Nigeria

    Anietie Edem Udokang

Authors
  1. Fidelis Ifeanyi Ugwuowo
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  2. Anietie Edem Udokang
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Corresponding author

Correspondence to Fidelis Ifeanyi Ugwuowo .

Editor information

Editors and Affiliations

  1. Applied Statistics Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Ashis SenGupta

  2. Department of Statistics, University of California, Riverside, CA, USA

    Barry C. Arnold

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Ugwuowo, F.I., Udokang, A.E. (2022). Modelling Circular Time Series with Applications. In: SenGupta, A., Arnold, B.C. (eds) Directional Statistics for Innovative Applications. Forum for Interdisciplinary Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-19-1044-9_22

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