Crude oil price fluctuations affect almost every individual and activity on the planet. Forecasting the crude oil price is therefore an important concern especially in economic policy and financial circles as it enables stakeholders estimate crude oil price at a point in time. Autoregressive integrated moving average (ARIMA) has been an effective tool that has been used widely to model time series. Its limitation is the fact that it cannot model nonlinear systems sufficiently. This paper assesses the ability to build a robust forecasting model for the world crude oil price, Brent on the international market using a hybrid of two methods ARIMA and polynomial harmonic group method of data handling. ARIMA methodology is used to model the time series component with constant variance whilst the polynomial harmonic group method of data handling is used to model the harmonic ARIMA model residuals.

The global demand for coconut and coconut-based products has increased rapidly over the past decades. Coconut price continues to fluctuate; thus, it is not easy to make predictions. Good price modelling is important to accurately predict the future coconut price. Several studies have been conducted to predict the price of coconut using various models. One of the most important and widely used models in time series forecasting is the autoregressive integrated moving average (ARIMA). However, price fluctuations is considered a problem with uncertain behaviour. The existing ARIMA time series model is unsuitable for solving this problem, because of the nonlinear series. Artificial neural networks (ANN) have been an effective method in solving nonlinear data pattern problems in the last two decades. The non-linear autoregressive neural network (NARNET) gives good forecast, most especially when series are non-linear. Therefore ARIMA-NARNET is considered a universal approach to forecasting the coconut price. The aim of the study is to establish a linear and nonlinear model in time series to forecast coconut prices. The ability of a hybrid approach that combines ARIMA and NARNET(ANN) models is investigated. Based on the experimental study, the experimental results show that the proposed method ARIMA-NARNET, is better at forecasting the price of coconut, an agriculture commodity, than both the ARIMA model and NARNET models. The expected benefit of the proposed forecasting model is it can help farmers, exporters, and the government to maximize profits in the future.INDEX TERMS ARIMA-NARNET, intelligent hybrid, coconut price, forecasting, time series.

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