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
Journal of Applied & Computational Mathematics received 1282 citations as per Google Scholar report