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All Journal Sciencestatistics: Journal of Statistics, Probability, and Its Application
Ulfa Putri Rahmani
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Penerapan Model Generalized Space Time Autoregressive (GSTAR) pada Data Inflasi Beberapa Kota Ulfa Putri Rahmani; Khoirin Nisa; Nurmaita Hamsyiah
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 3 No. 1 (2025): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v3i1.7526

Abstract

Model yang umum digunakan untuk data space time adalah model Vector autoregressive (VAR), Space Time Autoregressive (STAR), dan Generalized Space Time Autoregressive (GSTAR). Untuk lokasi yang memiliki karakteristik yang berbeda (heterogen), model GSTAR lebih baik digunakan dibandingkan model STAR. Tujuan dari penelitian ini adalah menerapkan model GSTAR pada data time series dari tiga lokasi berbeda. Data yang digunakan pada penelitian ini adalah data inflasi Palembang, Bandar Lampung, dan DKI Jakarta bulan Januari 2012 hingga Juni 2019. Bobot Lokasi yang digunakan adalah bobot lokasi invers jarak dan bobot lokasi normalisasi korelasi silang. Pada penelitian ini pendugaan parameter dilakukan dengan metode Generalized Least Square (GLS). Dari hasil analisis diperoleh model yang terbaik adalah model GSTAR(11) dengan bobot lokasi invers jarak karena memiliki rata-rata RMSE terkecil yaitu 0.467767. The models commonly used for space time data are the Vector autoregressive (VAR), Space Time Autoregressive (STAR), and Generalized Space Time Autoregressive (GSTAR) models. For locations that have different (heterogeneous) characteristics, the GSTAR model is better to use than the STAR model. The aim of this research is to apply the GSTAR model to time series data from three different locations. The data used in this research is inflation data from Palembang, Bandar Lampung, and DKI Jakarta from January 2012 to June 2019. The location weights used are distance inverse location weights and cross-correlation normalized location weights. In this research, parameter estimation was carried out using the Generalized Least Square (GLS) method. From the analysis results, it was found that the best model was the GSTAR(11) model with inverse distance location weights because it had the smallest average RMSE, namely 0.467767.
Co-Authors Khoirin Nisa Nurmaita Hamsyiah