An Autoregressive Moving Average Model for Short Term Prediction of Non-Insulin Dependent Diabetes Among Farmers in Benue State

Abstract

This study employs an Autoregressive Moving Average (ARMA) time series model to forecast the short-term incidence of non-insulin-dependent diabetes mellitus (Type 2 Diabetes) among farmers in Benue State, Nigeria. The data was collected from the Benue State Epidemiological Unit, Makurdi, and covered a 20-year period from January 2005 to June 2025. The study employed descriptive statistics and normality measures, Augmented Dickey-Fuller (ADF) unit root test and ARMA (p,q) model as the principal analytical techniques and procedures used to examine the data. The descriptive statistics indicated moderate variability in diabetes cases over the years, while the Augmented Dickey-Fuller (ADF) test confirmed the stationarity of the series in level. Model choice based on Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Hannan–Quinn Criterion (HQC) identified the ARMA(3,3) model as the best fit for forecasting diabetic cases in the study area.