Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

被引：76
作者：
Ruiz-Aguilar, J. J. [1 ]
Turias, I. J. [1 ]
Jimenez-Come, M. J. [1 ]
机构：
[1] Univ Cadiz, Polytech Sch Engn Algeciras, Intelligent Modelling Syst Res Grp, Cadiz 11202, Spain
来源：
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW | 2014年 / 67卷
关键词：
Inspection forecasting; Artificial neural networks; SARIMA; Hybrid models; FEEDFORWARD NETWORKS; ARIMA MODEL; FLOW; PREDICTION; CHOICE;
D O I：
10.1016/j.tre.2014.03.009
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
In this paper, the number of goods subject to inspection at European Border Inspections Post are predicted using a hybrid two-step procedure. A hybridization methodology based on integrating the data obtained from autoregressive integrated moving averages (SARIMA) model in the artificial neural network model (ANN) to predict the number of inspections is proposed. Several hybrid approaches are compared and the results indicate that the hybrid models outperform either of the models used separately. This methodology may become a powerful decision-making tool at other inspection facilities of international seaports or airports. (C) 2014 Elsevier Ltd. All rights reserved.
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页码：1 / 13
页数：13
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