APPLICATION OF THE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) METHOD IN FORECASTING THE CONSUMER PRICE INDEX IN FIVE CITIES OF SOUTH SULAWESI PROVINCE
Abstract
Changes in the Consumer Price Index (CPI) over time reflect the rate of increase (inflation) or decrease (deflation) of goods and services for daily household needs. The CPI and inflation serve as barometers for economic growth stability, as controlled inflation can increase people's purchasing power over time. According to the Central Statistics Agency (2023), in December, the year-on-year (y-o-y) inflation for five cities in South Sulawesi (Bulukumba, Watampone, Makassar, Parepare, and Palopo) was 2.81 percent, with a CPI of 117.35. Of the five cities, the highest y-o-y inflation occurred in Makassar at 2.89 percent, with a CPI of 117.49, while the lowest y-o-y inflation occurred in Palopo at 2.21 percent, with a CPI of 115.60. CPI forecasting is one way to predict future inflation values. This study aims to develop the best GSTAR model for forecasting CPI data for five cities in South Sulawesi, a topic that has not been extensively covered in previous research. The goal is to provide valuable information for maintaining CPI stability in South Sulawesi and to support the formulation of better economic policies. The study focuses on five cities within South Sulawesi, where direct relationships between cities are possible, allowing the spatial model to be limited to the first-order. The data used in this study consists of monthly CPI data from January 2014 to March 2023. The location weights used in the model include uniform weights, inverse distances, and normalized cross-correlations. The model development steps include testing for data stationarity, determining the space-time sequence, calculating location weights, estimating parameters, testing model adequacy, comparing Root Mean Square Error (RMSE), and selecting the best model for forecasting. The best GSTAR model found is GSTAR (1;1)-I(2) with inverse distance weighting, which yielded the smallest RMSE value. The results show that the forecasted values closely match the actual values for each city from March to September 2023.
Downloads
References
Aswi and Sukarna, Analisis Deret Waktu: Teori dan Aplikasinya, 2th ed. Andira Publisher, Makassar, 2017.
C. A. Melyani, A. Nurtsabita, G. Z. Shafa, and E. Widodo, “Peramalan Inflasi Di Indonesia Menggunakan Metode Autoregressive Moving Average (Arma),” J. Math. Educ. Sci., vol. 4, no. 2, pp. 67–74, 2021, doi: 10.32665/james.v4i2.231.
G. M. Box, G. E., Jenkins, G. M., Reinsel, G. C., Ljung, Time Series Analysis Forecasting and Control, 5th ed. Wiley, 2015.
W. W. S. Wei, Multivariate Time Series Analysis and Aplications, 1th ed. Wiley, 2019. doi: DOI:10.1002/9781119502951.
P. R. Arum, A. R. Indriani, and M. Al Haris, “Forecasting the Consumer Price Index With Generalized Space-Time Autoregressive Seemingly Unrelated Regression (Gstar-Sur): Compromise Region and Time,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 2, pp. 1183–1192, 2023, doi: 10.30598/barekengvol17iss2pp1183-1192.
F. B. Wijaya, I. M. Sumertajaya, and Erfiani, “Comparison of Autoregressive (AR), Vector Autoregressive (VAR), Space Time Autoregressive (STAR), and Generalized Space Time Autoregressive (GSTAR) in forecasting (Case: Simulation study with autoregressive pattern),” Int. J. Appl. Eng. Res., vol. 10, no. 21, pp. 42388–42395, 2015.
A. S. Mansoer, Tarno, and Y. Wilandari, “Pemodelan Seasonal Generalized Space Time Autoregressive (SGSTAR),” Gaussian, vol. 5, no. 4, pp. 593–602, 2016.
A. D. Sulistyono, H. Hartawati, N. W. Suryawardhani, A. Iriany, and A. Iriany, “Cross-Covariance Weight of GSTAR-SUR Model for Rainfall Forecasting in Agricultural Areas,” CAUCHY J. Mat. Murni dan Apl., vol. 6, no. 2, pp. 49–57, 2020, doi: 10.18860/ca.v6i2.7544.
M. S. Akbar et al., “A Generalized Space-Time Autoregressive Moving Average (GSTARMA) Model for Forecasting Air Pollutant in Surabaya,” J. Phys. Conf. Ser., vol. 1490, no. 1, 2020, doi: 10.1088/1742-6596/1490/1/012022.
A. N. Islamiyah, W. Rahayu, and E. D. Wiraningsih, “Pemodelan Generalized Space Time Autoregressive (GSTAR) dan Penerapannya pada Penderita TB Paru (BTA+) di DKI Jakarta,” J. Stat. dan Apl., vol. 2, no. 2, pp. 36–48, 2018, doi: 10.21009/jsa.02205.
M. I. T. Mario, Kartiko, and R. D. Bekti, “Pemodelan Generalized Space Time Autoregressive (Gstar) Untuk Peramalan Tingkat Inflasi Di Pulau Jawa,” J. Stat. Ind. dan Komputasi, vol. 06, no. 02, pp. 171–184, 2021.
F. N. Aryani, S. S. Handajani, and E. Zukhronah, “Application of Generalized Space Time Autoregressive Model on Farmer Exchange Rate Data in Three Provinces of The Sumatera Island,” J. ILMU DASAR, vol. 21, no. 2, p. 97, 2020, doi: 10.19184/jid.v21i2.17226.
Nur’Eni, D. Lusiyanti, and I. Gunawan, “Identifikasi Model Generalized Space-time Autoregressive (GSTAR) untuk Nilai Inflasi di Pulau Sulawesi,” J. Ilm. Mat. Dan Terap., vol. 18, no. 1, pp. 75–83, 2021, doi: 10.22487/2540766x.2021.v18.i1.15522.
A. T. subekti, “Kajian Inflasi Dan Dampaknya Terhadap Pertumbuhan Ekonomi Di Kota Jambi Tahun 2022 Study Of Inflation And Its Impact On Economic Growth In The City Of Jambi In 2022,” Khanza Intelekt., vol. 7, pp. 1–14, 2023, [Online]. Available: https://doi.org/10.37250/newkiki.v4i1.200
A. Fitri, A. F. Zohra, and M. H. Nasution, “Peramalan Laju Inflasi Bulanan Kota Padang Menggunakan Metode Triple Exponential Smoothing,” vol. 21, no. 2, pp. 1–10, doi: 10.22437/jiseb.v21i2.6050.
D. Lusiyanti et al., “Identifikasi Model Generalized Space- Time Autoregressive ( Gstar ) Untuk Nilai Inflasi Di Pulau Sulawesi,” vol. 18, pp. 75–83, 2021.
BPS, “indeks harga konsumen 90 kota di indonesia (2018=100).” [Online]. Available: https://www.bps.go.id/id/publication/2023/04/06/70bd1eeae8327d209f9d3340/indeks-harga-konsumen-90-kota-di-indonesia-2022--2018-100-.html
BPS, “Berita Resmi Statistik,” Bps.Go.Id, p. 1, 2024, [Online]. Available: https://sulsel.bps.go.id/id/pressrelease/2024/01/02/785/desember-2023--inflasi-year-on-year--yoy--gabungan-lima-kota-ihk-di-sulawesi-selatan-sebesar-2-81-persen.html
D. R. Prama, T. Notapiri, and B. N. Ruchjana, “Model Space-Time Autoregressive Integrated (STARI) pada Peramalan Indeks Harga Konsumen (IHK) di Kota Bogor, Depok, dan Bekasi,” Stat. J. Theor. Stat. Its Appl., vol. 22, no. 1, pp. 65–76, 2022, doi: 10.29313/statistika.v22i1.1086.
A. P. Muzdhalifah, T. Tarno, and P. Kartikasari, “Penerapan Model Generalized Space Time Autoregressive (Gstar) Untuk Meramalkan Penerbangan Domestik Pada Tiga Bandar Udara Di Pulau Jawa,” J. Gaussian, vol. 11, no. 3, pp. 332–343, 2023, doi: 10.14710/j.gauss.11.3.332-343.
E. Siswanto, H. Yasin, and S. Sudarno, “Pemodelan Generalized Space Time Autoregressive (Gstar) Seasonal Pada Data Curah Hujan Empat Kabupaten Di Provinsi Jawa Tengah,” J. Gaussian, vol. 8, no. 4, pp. 418–427, 2019, doi: 10.14710/j.gauss.v8i4.26722.
Copyright (c) 2025 Ahmad Zaki, Lutfiah Shafruddin, Irwan Thaha
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
