eprintid: 19280
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/92/80
datestamp: 2024-06-04 14:11:44
lastmod: 2024-06-04 14:11:44
status_changed: 2024-06-04 14:05:19
type: article
metadata_visibility: show
creators_name: Abdullah, 
creators_name: Sarpong-Streetor, R.M.N.Y.
creators_name: Sokkalingam, R.
creators_name: Othman, M.
creators_name: Azad, A.S.
creators_name: Syahrantau, G.
creators_name: Yusriwarti, 
creators_name: Arifin, Z.
title: Intelligent Hybrid ARIMA-NARNET Time Series Model to Forecast Coconut Price
ispublished: pub
keywords: Agriculture; Forecasting; Time series, Arimum-narnet; Auto-regressive; Autoregressive neural networks; Coconut price; Intelligent hybrid; Moving averages; Neural network model; Non linear; Times series; Times series models, Neural network models
note: cited By 3
abstract: 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. © 2013 IEEE.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161282451&doi=10.1109%2fACCESS.2023.3275534&partnerID=40&md5=e5878cb6b7999ce26cf83cadef65ff28
id_number: 10.1109/ACCESS.2023.3275534
full_text_status: none
publication: IEEE Access
volume: 11
pagerange: 48568-48577
refereed: TRUE
citation:   Abdullah and Sarpong-Streetor, R.M.N.Y. and Sokkalingam, R. and Othman, M. and Azad, A.S. and Syahrantau, G. and Yusriwarti and Arifin, Z.  (2023) Intelligent Hybrid ARIMA-NARNET Time Series Model to Forecast Coconut Price.  IEEE Access, 11.  pp. 48568-48577.