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Memprediksi dan memodelkan inflasi di Indonesia dengan metode autoregresif moving average (ARMA), calendar variation dan feedforward neural networks (FFANN)

Ronny Wicaksono; Cahyanto Budi, supervisor (Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2006)

 Abstrak

The feed forward neural network (FFANN) model has been the most popular form of artificial neural network model used for forecasting, particularly in economics and finance. In this paper, we elucidate the application of FFANN as a means of modeling financial data. We particularly focus on the model building of FFANN as time series model and use inflation rates in Indonesia as a case study. A comparison is drawn between FFANN model and the best existing models based on traditional econometrics time series approach. The best models are selected on forecasting ability by using the MSE, particularly on the dynamic forecast. The results show that FFANN models outperform the traditional econometric time series model.

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 Metadata

No. Panggil : T18415
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Entri tambahan-Nama badan :
Subjek :
Penerbitan : Depok: Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2006
Program Studi :
Bahasa : ind
Sumber Pengatalogan :
Tipe Konten :
Tipe Media :
Tipe Carrier :
Deskripsi Fisik :
Naskah Ringkas :
Lembaga Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 3
  • Ketersediaan
  • Ulasan
No. Panggil No. Barkod Ketersediaan
T18415 15-20-033738778 TERSEDIA
Ulasan:
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