GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA PERAMALAN DATA TIME SERIES
General Regression Neural Network (GRNN) is one method that was developed from the concept of artificial neural network that can be used for forecasting. This method was applied to predict the time series data that has a causal relations where the forecasting method used previously (ARIMA BOXJenkins...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | Theses and Dissertations NonPeerReviewed |
| منشور في: |
[Yogyakarta] : Universitas Gadjah Mada
2012
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://repository.ugm.ac.id/99085/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=55212 |
| الوسوم: |
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| الملخص: | General Regression Neural Network (GRNN) is one method that was
developed from the concept of artificial neural network that can be used for
forecasting. This method was applied to predict the time series data that has a
causal relations where the forecasting method used previously (ARIMA BOXJenkins)
is not able to explain the presence of linkage data.
This research was conducting by taking the dollar exchage rate and
composite stock price index(IHSG). By using the GRNN methode will obtained
the predictive value in some future periode. The advantages using this method is
faster in term of computation and doesn�t requared the presence of a data
asumptions. GRNN method produces more accurate predictive value comapred
with ARIMA. It was shown that the MSE value is smaller than ARIMA |
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