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

Xiaoping Zhan (), Tiefeng Ma, Shuangzhe Liu and Kunio Shimizu
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Xiaoping Zhan: Sichuan University
Tiefeng Ma: Southwestern University of Finance and Economics
Shuangzhe Liu: University of Canberra
Kunio Shimizu: The Institute of Statistical Mathematics

Journal of Agricultural, Biological and Environmental Statistics, 2018, vol. 23, issue 3, No 6, 410-425

Abstract: Abstract 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.

Keywords: Circular regressive model; Mean circular prediction error; Non-continuous wind direction; Prediction accuracy (search for similar items in EconPapers)
Date: 2018
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