Forecasting of crude palm oil price using hybridizing wavelet and group method of data handling model

Abstract

Forecasting of Crude Palm Oil (CPO) is one of the most important and the largest vegetable oil traded in the world market. This study investigates the forecasting of Crude Palm Oil (CPO) price using a hybrid model of Group Method of Data Handling (GMDH) with wavelet decomposition. The original monthly data of CPO time series were decomposed into the spectral band. After that, these decomposed subseries were given as input time series data to GMDH model to forecast the CPO price of monthly time series data. The result performance of hybridized GMDH model is compared with the original GMDH model. The measurements results from the mean absolute error (MAE) and the root mean square error (RMSE) showed that the hybrid GMDH model with wavelet decomposition gives more accurate result of predictions compared with the original GMDH model.