Forecasting comparisons using a hybrid ARFIMA and LRNN models. Issue 8 (14th September 2018) - Pwasong, Augustine; Sathasivam, Saratha
ABSTRACT: In this article, an autoregressive fractionally integrated moving average model ( ARFIMA ) and a layer recurrent neural network ( LRNN ) were combined to form a hybrid forecasting model. The hybrid model was applied on the daily crude oil production data of the Nigerian National Petroleum Corporation ( NNPC ) to forecast the daily crude oil production of the NNPC . The Bayesian model averaging technique was used to obtain a combined forecast from the two separate methods. A comparison was made between the hybrid model with standalone ARFIMA and LRNN methods in which the hybrid model produced better forecasting performance than the standalone methods.