Performance Comparison of Auto-Regressive Moving Average (ARIMA) and Artificial Neural Networks (ANN) in Ginger Price and Output Forecasting in Nigeria (2021–2025)

This study analyzed and forecasted ginger output and price trends in Nigeria using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models. Secondary data on ginger production and price covering the period 1990–2020 were obtained from the Food and Agriculture Organization (FAO) and the National Agricultural Extension and Research Liaison Services (NAERLS). The data were analyzed to generate forecasts for the period 2021–2025. Stationarity of the time series was tested using the Augmented Dickey–Fuller (ADF) and Phillips–Perron tests, which indicated that both output and price series became stationary after first differencing. Model identification was conducted using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). The most suitable ARIMA models selected were ARIMA (6,1,5) for ginger output and ARIMA (9,1,5) for ginger price based on model diagnostics such as AIC, SBIC, R-squared, and volatility measures. For ANN forecasting, optimal network structures identified were 1–10–1 for ginger output and 1–16–1 for ginger price, which produced the lowest network errors. Forecast evaluation showed that both ARIMA and ANN models produced relatively low forecast errors, indicating good predictive performance. However, the ANN model demonstrated slightly higher forecasting accuracy than the ARIMA model for both ginger output and price. Forecast results indicate a steady increase in ginger production from about 608,080 metric tonnes in 2021 to 719,968 metric tonnes by 2025, while prices are projected to rise gradually over the same period. The findings suggest growing demand for ginger at local and international market.