Model Generalized Space Time Autoregressive (GSTAR) dengan Analisis Data Menggunakan Software R

Talungke, Yulianti; Nainggolan, Nelson; Hatidja, Djoni

doi:10.35799/dc.4.2.2015.8649

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Pada model STAR, parameternya diasumsikan tidak bergantung pada lokasi. Sedangkan pada model GSTAR, parameter model diasumsikan dapat berubah-ubah untuk setiap lokasi yang berbeda [19].…”

Section: Model Generalized Space-time Autoregressive (Gstar)unclassified

Peramalan Nilai Tukar Rupiah Terhadap Dolar Singapura, Baht, Dan Peso Menggunakan Metode Gstar

Budiarti,

Rahmawati,

Septyanto

et al. 2024

MILANG

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The Generalized Space-Time Autoregressive (GSTAR) model is an extension of the Space-Time Autoregressive (STAR) model. The difference between the two models lies in the parameter assumptions. In the STAR model, the parameters are assumed to be independent of location, so this model is only suitable for data with homogeneous locations. Meanwhile in the GSTAR model, the parameters are assumed to change for each different location. This research aims to develop the best model for forecasting the Rupiah exchange rate against the Singapore Dollar, Thai Baht, and Philippine Peso. The appropriate model used for the Rupiah exchange rate data is the GSTAR(51)I(1) model. The weights used in this study are uniform location weights and inverse distance. The modeling results show that the best model is the model with inverse distance weighting, which has an MSE value of 371.8907 with MAPE values for each of the Rupiah exchange rate data against the Singapore Dollar, Thai Baht, and Philippine Peso of 0.3154214%, 0.8369436%, and 0.6237245%, respectively.

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“…Pada model STAR, parameternya diasumsikan tidak bergantung pada lokasi. Sedangkan pada model GSTAR, parameter model diasumsikan dapat berubah-ubah untuk setiap lokasi yang berbeda [19].…”

Section: Model Generalized Space-time Autoregressive (Gstar)unclassified

Peramalan Nilai Tukar Rupiah Terhadap Dolar Singapura, Baht, Dan Peso Menggunakan Metode Gstar

Budiarti,

Rahmawati,

Septyanto

et al. 2024

MILANG

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Pemodelan Indeks Harga Properti Residensial Di Indonesia Menggunakan Metode Generalized Space Time Autoregressive

Aufa

Santoso

Suparti

2022

J.Gauss

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Generalized Space Time Autoregressive (GSTAR) is a model used for space time data analysis. Space time data is data related to events at previous times and different locations. GSTAR is an expansion of the Space Time Autoregressive (STAR) method. The STAR method is only suitable for homogeneous locations while GSTAR can be used for heterogeneous locations. This research uses Residensial Property Price Index (IHPR) data. IHPR data is in the form of a multivariate time series consisting of 18 cities/regions with a certain time span. In this study, the analysis of IHPR data is carried out by looking at the relationship between the previous time and other cities/regions. Therefore, the method that can be used is GSTAR method. Analysis of IHPR data in each city/region can help increase the supply of housing, thereby reducing the number of backlogs. The backlog of houses in Indonesia is still relatively high. Backlog is an indicator that is often used by the government to measure the number of housing needs in Indonesia. Based on the fulfillment of the assumptions and the smallest MSE value, the best model obtained is GSTAR(4;1,1,1,1) using cross-correlation normalized weight. The largest IHPR data on forcasting results is in the cities of Makassar, Manado, and Surabaya while the smallest IHPR data is in the city of Balikpapan. The GSTAR method produces forcasted data that is close to the actual data so it is good to use.Keywords : GSTAR, OLS, IHPR

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Model Generalized Space Time Autoregressive (GSTAR) dengan Analisis Data Menggunakan Software R

Cited by 2 publications

References 2 publications

Peramalan Nilai Tukar Rupiah Terhadap Dolar Singapura, Baht, Dan Peso Menggunakan Metode Gstar

Peramalan Nilai Tukar Rupiah Terhadap Dolar Singapura, Baht, Dan Peso Menggunakan Metode Gstar

Pemodelan Indeks Harga Properti Residensial Di Indonesia Menggunakan Metode Generalized Space Time Autoregressive

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