MODEL RUNTUN WAKTU UNTUK MEMODELKAN DATA DERET BERKALA JANGKA PANJANG
There are two types of data are known in the analysis of time series that are short memory and long memory. Short memory is data that has a characteristic process of short-term memory while long memory is data that has a characteristic process of long-term memory. The long memory data is only capabl...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | Theses and Dissertations NonPeerReviewed |
| منشور في: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://repository.ugm.ac.id/132351/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72881 |
| الوسوم: |
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| الملخص: | There are two types of data are known in the analysis of time series that
are short memory and long memory. Short memory is data that has a characteristic
process of short-term memory while long memory is data that has a characteristic
process of long-term memory. The long memory data is only capable of long
accurately analyzed using ARFIMA (Autoregressive Fractionally Integrated
Moving Average). The purpose of this thesis to explain how to do data modeling
with appropriate ARFIMA method with steps of data analysis by the method of
Box Jenkins were able to apply these forecasting methods to real data. Data time
series that are characterized by long memory has autocorrelation function plot
(Autocorrelation Function (ACF)) which does not go down exponentially but
slowly declining or hyperbolic.
ARFIMA model is the development of models Autoregressive Integrated
Moving Average Model ( ARIMA ), with the value of the distinction (
differencing ) is real value. In writing this essay, identification of order parameter
is done by looking at the ACF and PACF correlogram. ARFIMA modeling steps
are as follows: 1. normality test for long memory data |
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