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

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Article

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

J.J. Ruiz-Aguilar - Nama Orang; I.J. Turias - Nama Orang; M.J. Jiménez-Come - Nama Orang;

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.

Ketersediaan

IPC Corporate University Library ATC PO LAR h C.1

ATC1602697

Tersedia

Informasi Detail

Judul Seri: Transportation Research Part E
No. Panggil: ATC PO LAR h
Penerbit: Spain : Elsevier., 2014
Deskripsi Fisik: 13 p.
Bahasa: English
ISBN/ISSN: 1366-5545
Klasifikasi: PO
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: online resource
Edisi: -
Subjek: Inspection forecasting
Artificial neural networks
SARIMA
Hybrid models
Info Detail Spesifik: -
Pernyataan Tanggungjawab: J.J. Ruiz-Aguilar

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