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Aisha Shaliha Mansoer
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PEMODELAN SEASONAL GENERALIZED SPACE TIME AUTOREGRESSIVE (SGSTAR) (Studi Kasus: Produksi Padi di Kabupaten Demak, Kabupaten Boyolali, dan Kabupaten Grobogan) Aisha Shaliha Mansoer; Tarno Tarno; Yuciana Wilandari
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.045 KB) | DOI: 10.14710/j.gauss.v5i4.14716

Abstract

Generalized Space Time Autoregressive (GSTAR) model is more flexible as a generalization of Space Time Autoregressive (STAR) model which be able to express the linear relationship of time and location. The purpose of this study is to construct GSTAR model for forecasting the rice plant production in the three districts of Central Java. The data which used to contruct the model is quarterly data of rice plant production in Demak, Boyolali and Grobogan from 1987 through 2014. According to the empirical study result using GSTAR model with uniform weight, binary weight, inverse distance wight, and normalized cross correlation weight, GSTAR (31)-I(1)3 with uniform weight is the optimal model. The model shows that every location is influenced by the location itself. Keywords :  GSTAR, Space Time, uniform weight
Co-Authors Tarno Tarno Yuciana Wilandari