TIME SERIES ANALYSIS OF SALES OF RICE
Abstract
This study analyzed the sales of rice in Nigeria from 2017 to 2024 with the objective of
modelling and forecasting future sales patterns using time series analysis. The study
examined trends and seasonal patterns in rice sales, investigated stationarity, and developed
an appropriate forecasting model. Exploratory analysis revealed high variability in sales
values. Seasonal decomposition identified festive-related increases in December and dips
in February, while the trend-cycle showed moderate fluctuations, including a decline in
2020 and 2021 attributed to economic disruptions. Stationarity tests confirmed the
suitability of autoregressive models, and among the competing models, the ARMA(1,0)
was identified as the best fit based on the Bayesian Information Criterion. Diagnostic
checks validated the adequacy of the model, with no evidence of significant autocorrelation
or outliers. Forecasts for 2025 to 2029 indicated that rice sales would remain relatively
stable. The findings provide important insights for policymakers, traders, and stakeholders
in planning and decision-making related to the rice market in Nigeria.
Keywords: Rice sales, Time series analysis, ARMA model, Forecasting, Nigeria,
Stationarity
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