Forecasting Food Inflation in Nigeria using ARIMA and ELM Techniques

Saminu Umar, M. K. Aminu

Abstract


This study focuses on modelling monthly food inflation rate in Nigeria using Autoregressive integrated moving average (ARIMA) model and Extreme Learning Machine (ELM) algorithm. ARIMA (2, 1, 2) was found out to be the best fitted among the considered ARIMA models model based on its minimum values of AIC and BIC. Forecast of Nigerian food inflation rate obtained from the selected ARIMA model and ELM were compared in terms of, MAE MSE and RMSE, the computational results on the data demonstrate that the ELM provides better forecasts than the RIMA model. The study, therefore, recommends that stakeholders and authorities should think in the line of using ELM in conducting food inflation forecasts in Nigeria given its higher predictive and forecasting ability.

Keywords


Time Series, Inflation, ARIMA, Feedforward Neural Networkss, Extreme Learning Machine.

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References


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DOI: http://dx.doi.org/10.52155/ijpsat.v29.2.3751

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