Asian Journal of Probability and Statistics

Accurate and reliable information on weather and climate is crucial to support the smooth operation of various sectors, including agriculture, aviation, transportation, water resources management, and disaster risk reduction. These sectors are highly vulnerable to climate variability and change, where extreme weather events such as storms, floods, and droughts have devastating impacts. The purpose of this study is to model climatic data, such as rainfall, humidity, wind speed, temperature, using a Seasonal Autoregressive Integrated Moving average Vector (SARIMAV) model. Seasonal monthly Rainfall, humidity, wind speed and temperature data in Akwa Ibom State were collated from 2004-2024 for the research. The autocorrelation and partial autocorrelation functions of the differenced series suggested different orders of SARIMAV models for the four Climate data. Ordinary least squares was adopted to estimate parameters of the models. The analysis produced two sets of models; the SARIMAV models suggested from the correlogram and its reduced form to ensure model invertibity, From the MSE, AIC and BIC, the reduced parameter models outperformed the earlier SARIMAV models.  The study's findings provide valuable insights for climate forecasting and decision-making in the region.