Accurate estimation and forecasting of gasoline is vital for policy and decision-making process in energy sector. This paper presents a hybrid data-driven model based on Artificial Neural Network (ANN) and autoregressive integrated moving average (ARIMA) approach for optimum estimation and forecasting of gasoline consumption. The proposed hybrid ARIMA-ANN approach considers six lagged variables and one forecasted values provided by ARIMA process. The ANN trains and tests data with Multi Layer Perceptron (MLP) approach which has the lowest Mean Absolute Percentage Error (MAPE). To show the applicability and superiority of the proposed hybrid approach, daily available data were collected for 7 years (2005–2011) in Iran. Although eliminating subside from gasoline price has led to appearing noisy data in gasoline consumption in Iran the acquired results show high accuracy of about 9427% by using the proposed hybrid ARIMA-ANN method. The results of the proposed model are compared respect to regression’s models and ARIMA process. The outcome of this paper justifies the capability of the proposed hybrid ARIMA-ANN approach in accurate forecasting gasoline consumption.
Accepté le :
DOI : 10.1051/ro/2016059
Mots-clés : Gasoline consumption, artificial neural networks, ARIMA, forecasting, multi layer perceptron
@article{RO_2017__51_3_719_0,
author = {Babazadeh, Reza},
title = {A {Hybrid} {ARIMA-ANN} approach for optimum estimation and forecasting of gasoline consumption},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {719--728},
publisher = {EDP-Sciences},
volume = {51},
number = {3},
year = {2017},
doi = {10.1051/ro/2016059},
mrnumber = {3880521},
zbl = {06842843},
language = {en},
url = {http://archive.numdam.org/articles/10.1051/ro/2016059/}
}
TY - JOUR AU - Babazadeh, Reza TI - A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2017 SP - 719 EP - 728 VL - 51 IS - 3 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro/2016059/ DO - 10.1051/ro/2016059 LA - en ID - RO_2017__51_3_719_0 ER -
%0 Journal Article %A Babazadeh, Reza %T A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption %J RAIRO - Operations Research - Recherche Opérationnelle %D 2017 %P 719-728 %V 51 %N 3 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro/2016059/ %R 10.1051/ro/2016059 %G en %F RO_2017__51_3_719_0
Babazadeh, Reza. A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption. RAIRO - Operations Research - Recherche Opérationnelle, Tome 51 (2017) no. 3, pp. 719-728. doi : 10.1051/ro/2016059. http://archive.numdam.org/articles/10.1051/ro/2016059/
and , A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl. Soft Comput. 11 (2011) 2664–2675. | DOI
, Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (2003) 159–175. | DOI | Zbl
, and , An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments. Energy Policy 38 (2010) 1529–1536. | DOI
and , Radial basis neural network based islanding detection in distributed generation. Int. J. Eng. Trans. A: Basic 27 (2014) 1061–1070.
, and , Optimum estimation and forecasting of renewable energy consumption by artificial neural networks. Renew. Sustain. Energy Rev. 27 (2013) 605–612. | DOI
, , and , Gmdh type neural networks and their application to the identification of the inverse kinematics equations of robotic manipulators (Research note). Int. J. Eng. 18 (2005) 135–143. | Zbl
, , , , , and , A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmos. Environ. 42 (2008) 8331–8340. | DOI
J.D. Hamilton, Time series analysis. Princeton University Press, New Jersey (1994). | MR | Zbl
P. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control. Holden-day Inc., San Francisco, CA (1976). | MR | Zbl
, Insights into neural-network forecasting time series corresponding to ARMA (p; q) structures. Omega 29 (2001) 273–289. | DOI
, and , Model identification of ARIMA family using genetic algorithms. Appl. Math. Comput. 164 (2005) 885–912. | MR | Zbl
and , An ARMA order selection method with fuzzy reasoning. Signal Process. 81 (2001) 1331–1335. | DOI | Zbl
M. Turhan, Neural networks and computation of neural network weights and biases by the generalized delta rule and back-propagation of errors. Rock solid images (1995).
, and , Artificial neural network involved in the action of optimum mixed refrigerant (domestic refrigerator). Int. J. Eng. 26 (2013) 1025–2495.
P.I. Werbos, Beyond regression: new tools for prediction and analysis in the behavior sciences. Ph.D. thesis, Harvard University, Cambridge, MA (1974). | MR
D.E. Rumelhart and J.L. McClelland, Parallel distributed processing: explorations in the microstructure of cognition, Vol. 1. Cambridge, MA: Foundations, MIT Press (1986).
, and , Composite modeling for adaptive short term load forecasting. IEEE Trans. Power Syst. 6 (1991) 450–457. | DOI
and , An artificial neural network (p, d, q) model for time series forecasting. Expert Syst. Appl. 37 (2010) 479–489. | DOI
, , , and , An ARIMA-ANN Hybrid Model for Time Series Forecasting. Systems Res. Behavior. Sci. 30 (2013) 244–259. | DOI
Cité par Sources :
