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Forecasting analysis of consumer goods demand using neural networks and arima

Isti Surjandari, Muhammad Riefqi, Maya Arlini Puspasari (Faculty of Engineering, Universitas Indonesia, 2015)

 Abstrak

Accurate forecasting of consumer demand for goods is extremely important as it allows companies to provide the right amount of goods at the right time. Autoregressive integrated moving average (ARIMA) is a popular method for forecasting time series data, and previous studies have shown that ARIMA can produce fairly accurate forecasting results. On the other hand, the neural network method has advantages in detecting non-linear patterns in data. In addition to these methods, the hybrid method, which combines the ARIMA and neural network methods, was applied in this study. A comparison analysis was conducted to determine the best performing model. In this study, the neural network model was found to be the most accurate.

 Metadata

No. Panggil : UI-IJTECH 6:5 (2015)
Entri utama-Nama orang :
Subjek :
Penerbitan : Depok: Faculty of Engineering, Universitas Indonesia, 2015
Sumber Pengatalogan : LibUI eng rda
ISSN : 20869614
Majalah/Jurnal : International Journal of Technology
Volume : Vol. 6, No. 5, December 2015: Hal. 872-880
Tipe Konten : text
Tipe Media : unmediated
Tipe Carrier : volume
Akses Elektronik : https://doi.org/10.14716/ijtech.v6i5.1882
Institusi Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 4 R. Koleksi Jurnal
  • Ketersediaan
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No. Panggil No. Barkod Ketersediaan
UI-IJTECH 6:5 (2015) 08-23-00011723 TERSEDIA
Ulasan:
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