Markov-Switching Linked Autoregressive Model for Non-continuous Wind Direction Data

  • Xiaoping ZhanEmail author
  • Tiefeng Ma
  • Shuangzhe Liu
  • Kunio Shimizu


In this paper, a Markov-switching linked autoregressive model is proposed to describe and forecast non-continuous wind direction data. Due to the influence factors of geography and atmosphere, the distribution of wind direction is disjunct and multi-modal. Moreover, for a number of practical situations, wind direction is a time series and its dependence on time provides very important information for modeling. Our model takes these two points into account to give an accurate prediction of this kind of wind direction. A simulation study shows that our model has a significantly higher prediction accuracy and a smaller mean circular prediction error than three existing models and it is illustrated to be effective by analyzing real data. Supplementary materials accompanying this paper appear online.


Circular regressive model Mean circular prediction error Non-continuous wind direction Prediction accuracy 



We would like to thank the Editors and Reviewers very much for their many valuable comments and advice which led to a significantly improved presentation of the manuscript. The research by the first two authors was supported by 111 Project (Grant No. B18062) and the National Natural Science Foundation of China (Nos. 11471264, 11401148, 11571282) and Fundamental Research Funds for the Central Universities (Nos. JBK120509, JBK140507).

Supplementary material

13253_2018_331_MOESM1_ESM.r (2 kb)
Supplementary material 1 (R 2 KB)
13253_2018_331_MOESM2_ESM.xlsx (2.4 mb)
Supplementary material 2 (xlsx 2467 KB)


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Copyright information

© International Biometric Society 2018

Authors and Affiliations

  • Xiaoping Zhan
    • 1
    Email author
  • Tiefeng Ma
    • 2
  • Shuangzhe Liu
    • 3
  • Kunio Shimizu
    • 4
  1. 1.Law SchoolSichuan UniversityChengduChina
  2. 2.Center of Statistical Research, School of StatisticsSouthwestern University of Finance and EconomicsChengduChina
  3. 3.Faculty of Education, Science, Technology and MathematicsUniversity of CanberraCanberraAustralia
  4. 4.School of Statistical ThinkingThe Institute of Statistical MathematicsTokyoJapan

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