Memprediksi dan memodelkan inflasi di Indonesia dengan metode autoregresif moving average (ARMA), calendar variation dan feedforward neural networks (FFANN)
| Main Authors: | Ronny Wicaksono, author, Add author: Cahyanto Budi, supervisor |
|---|---|
| Format: | Masters Thesis |
| Terbitan: |
Fakultas Ekonomi dan Bisnis Universitas Indonesia
, 2006
|
| Subjects: | |
| Online Access: |
http://lontar.ui.ac.id/detail?id=107646 |
| ctrlnum |
107646 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><type>Thesis:Masters</type><title>Memprediksi dan memodelkan inflasi di Indonesia dengan metode autoregresif moving average (ARMA), calendar variation dan feedforward neural networks (FFANN)</title><creator>Ronny Wicaksono, author</creator><creator>Add author: Cahyanto Budi, supervisor</creator><publisher>Fakultas Ekonomi dan Bisnis Universitas Indonesia</publisher><date>2006</date><subject>Feedforward control systems</subject><subject>Neural networks (Computer science)</subject><description>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.</description><identifier>http://lontar.ui.ac.id/detail?id=107646</identifier><recordID>107646</recordID></dc>
|
| format |
Thesis:Masters Thesis Thesis:Thesis |
| author |
Ronny Wicaksono, author Add author: Cahyanto Budi, supervisor |
| title |
Memprediksi dan memodelkan inflasi di Indonesia dengan metode autoregresif moving average (ARMA), calendar variation dan feedforward neural networks (FFANN) |
| publisher |
Fakultas Ekonomi dan Bisnis Universitas Indonesia |
| publishDate |
2006 |
| topic |
Feedforward control systems Neural networks (Computer science) |
| url |
http://lontar.ui.ac.id/detail?id=107646 |
| contents |
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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Universitas Indonesia |
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51 |
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Perpustakaan Universitas Indonesia |
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492 |
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contoh Repository Tesis (Open) Universitas Indonesia |
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KOTA DEPOK |
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JAWA BARAT |
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IOS18066 |
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2022-12-14T02:44:31Z |
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17.610468 |