Time Series: Predicting Nigerian Food Prices using ARIMA Model and R-Programming

The majority of food commodities in Nigeria have seen persistent price instability. this is brought by elements like insecurity/insurgency, poor storage facilities, seasonal price changes, inconsistent government policies, COVID-19 containment measures, poor access to credit, technical inputs, lack of modern farm tools and implements. This study focused on comparing the prices of four different food items - beans, onion, tomato, and yam using the ARIMA model to forecast future prices. Two out of the six geopolitical zones of Nigeria were used for the study; the North-Central and North-West. The National Bureau of Statistics (NBS) provided the raw data between 2017 and 2018, and the items were weighed in kilograms (Kg). The data was extrapolated into a time series data by executing in R Studio. The stationarity of the series data was obtained by a Unit root Test using the KPSS test (If p<0.05 means the time series is stationary). Results from the forecasted values indicated that food commodities' prices increase with time, making ARIMA a good model for forecasting prices. It was recommended that necessary measures should be put in place to ameliorate the high cost of food prices being experienced in the country of Nigeria.