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

• Published in: Journal of agricultural, biological, and environmental statistics Vol. 23; no. 3; pp. 410 - 425
• Main Authors: Zhan, Xiaoping, Ma, Tiefeng, Liu, Shuangzhe, Shimizu, Kunio
• Format: Journal Article
• Language: English
• Published: New York Springer Science + Business Media 01.09.2018; Springer US; Springer; Springer Nature B.V
• Subjects: Agriculture; Animal behavior; Atmospheric models; Autoregressive models; Biostatistics; Computer simulation; Data processing; Geography; Health Sciences; Markov chains; Mathematics and Statistics; Medicine; Model accuracy; Monitoring/Environmental Analysis; prediction; Statistics; Statistics for Life Sciences; Switching; Time dependence; time series analysis; Wind; Wind direction; Prediction accuracy; Circular regressive model; Non-continuous wind direction; Mean circular prediction error

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 situati...