Time-series analysis demonstrates seasonal rainfall predictability using an ARIMA (3,1,2) model, highlighting key planning windows for agricultural production.
This study focuses on the use of Autoregressive Integrated Moving Average (ARIMA) to forecast rainfall pattern in Yola Nigeria. It is aimed at estimating the trend of the monthly rainfall data in Yola for a period of 26years (2000-2025) to determine the pattern of rainfall, find the best ARIMA model that fits the data to make forecast. Data for this study was collected through extraction from the records in the Nigeria Meteorological Agency, Yola International Airport Adamawa State. The data covered a period of 26years (2000-2025) giving a total of 312 monthly rainfall readings. The data was analysed using Autoregressive Integrated Moving Average (ARIMA). Of all the ARIMA models considered, ARIMA (3,1,2) gave the least Akaike Information Criterion value of 3180.448187. Hence, it was chosen as the best model to fit the data and make rainfall forecast for the twelve months in 2026. The result shows that rainfall commenced in April, 2026 and ended in October, 2026. The highest rainfall of 207.3589mm was recorded in August, 2026. The identified model is appropriate for short term forecast which will help both the government and farmers to make decisions that will enhance agricultural production and rain water management.
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Leslie et al. (2026) studied this question.
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