Journal of Applied & Computational Mathematics

ISSN: 2168-9679

Open Access

Mathew Kosgei

Department of Mathematics, Physics and Computing in the School of Sciences and Aerospace Studies, Moi University, Kenya

  • Research Article   
    Modeling Rainfall Data in Kenya Using Bayesian Vector Autoregressive
    Author(s): Gitonga Harun Mwangi*, Joseph Koske and Mathew Kosgei

    Time series modeling and forecasting has ultimate importance in various practical domains in the world. Many significant models have been proposed to improve the accuracy of their prediction. Global warming has been a big challenge to the world in affecting the normality of the day to day economic and non-economic activities. It causes far-reaching weather changes, which are characterized by precipitation or temperature fluctuations. Rainfall prediction is one of the most important and challenging tasks in the recent today’s world. In Kenya unstable weather patterns which are associated with global warming have been experienced to a greater extent. The objective of this study was to modeled rainfall patterns in Kenya by use of Bayesian Vector Autoregressive (BVAR). To achieve this objective, the data was first statistically diagnosed using Augmented Dicke.. Read More»
    DOI: 10.37421/2168-9679.2022.11.487

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