Integration of GSTAR-X and Uniform location weights methods for forecasting Inflation Survey of Living Costs in Central Java

Alwan Fadlurrohman(1*)


(1) Universitas Muhammadiyah Semarang
(*) Corresponding Author

Abstract


Inflation is a tendency to increase prices of goods and services that take place continuously. Inflation is a monthly time series data that is thought to be influenced by location elements. Modeling for inflation forecasting that involves time and location (spatio temporal) can use the Generalized Space Time Autoregressive (GSTAR) method. To increase accuracy in modeling and forecasting, the GSTAR model was developed into the GSTARX model by involving exogenous variables. Exogenous Variavel used in GSTARX modeling for forecasting Inflation is a variation of the Eid calendar. This GSTARX modeling is applied for inflation forecasting in six cities Cost of Living Survey (SBH) in Central Java, namely Cilacap, Purwokerto, Semarang, Kudus, Magelang and Surakarta. The purpose of this study is to get the best GSTARX model for inflation forecasting for six SBH cities in Central Java. The selection of the best model from the GSTARX method is seen with the smallest RMSE value of each model. Obtained that the GSTARX model with uniform weights is the best model because it has a smaller RMSE compared to the GSTARX model with inverse distance weights, the RMSE values are 0.6122 and 0.6137, respectively. It can be concluded that the GSTARX method with Uniform weighting can provide better performance and can be used to predict the inflation of the six SBH cities in Central Java in the next 12 periods.

Keywords


GSTAR; GSTARX; inflation; central java; cost of living survey

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References


Borovkova, S., Lopuhaä, H. P., & Ruchjana, B. N. (2008). Consistency and asymptotic normality of least squares estimators in generalized STAR models. Statistica Neerlandica, 62(4), 482–508. https://doi.org/10.1111/j.1467-9574.2008.00391.x

BPS. (2008). PERKEMBANGAN INDEKS HARGA KONSUMEN/INFLASI. BPS: Berita Resmi Statistik, September, 1–9.

Ismiatul, S., & Rosyida, I. (2019). Implementasi Model Fuzzy-Wavelet dan FIS Metode Mamdani dalam Prediksi Nilai Tukar EUR / IDR. PRISMA, Prosiding Seminar Nasional Matematika, 2, 313– 322.

Nurani, B. (2002). Pemodelan Kurva Produksi Minyak Bumi Menggunakan Model Generalisasi Star. Forum Statistika Dan Komputasi, September(September), 1–6.

Nurchayani, F. (2016). Pengelompokan Stasiun Hujan untuk Model Generalized Space Time Autoregressive (GSTAR) pada Peramalan Curah Hujan Kabupaten Jember dengan Tiga Pembobotan. Universitas Jember.

Ruchjana, B. N. (2019). Pengembangan Model Spatio Temporal. Prosiding Seminar Nasional Matematika Dan Pendidikan Matematika 2019, 1–19.

Suhartono, & Subanar. (2006). The Optimal Determination of Space Weight in GSTAR Model Using Cross-Correlation Inference. Journal of Quantitative Methods, 2(2), 45–53.

Wei, W. W. . (2013). Time Series Analysis. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods.Volume 2: Statistical Analysis (2nd ed., pp. 458–485). Oxford University Press.


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DOI: https://doi.org/10.26714/jichi.v1i1.5583

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Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
Organized by
Department of Informatics
Faculty of Engineering
Universitas Muhammadiyah Semarang

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