Forecasting comparisons using a hybrid ARFIMA and LRNN models. Issue 8 (14th September 2018)

• Record Type: Journal Article
• Authors: Pwasong, Augustine; Sathasivam, Saratha
• Is Part Of: Communications in statistics. Volume 47:Issue 8(2018)
• Page Start: 2286
• Page End: 2303
• Publication Date: 2018-09-14
• Subjects: Bayesian model averaging -- Autoregressive -- Neural network -- Mean absolute error -- Root mean square error and forecasting; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1341529
• Languages: English
• ISSNs: 0361-0918

Abstract

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.