FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME

  • Prizka Rismawati Arum Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, Indonesia
  • Anita Retno Indriani Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, Indonesia
  • M Al Haris Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss2pp1183-1192
Keywords: Central Java, Consumer Price Index, GSTAR-SUR, RMSE

Abstract

Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city

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References

S. Zahara, Sugianto, and M. B. Ilmiddaviq, “Consumer Price Index Prediction Using Long Short Term Memory (LSTM) Based Cloud Computing,” ICTVT 2019, 2020, doi: 10.1088/1742-6596/1456/1/012022.

D. U. Wutsqa, R. Kusumawati, and R. Subekti, “The Application of Elman Recurrent Neural Network Model for Forecasting Consumer Price Index of Education , Recreation and Sports in Yogyakarta,” in 10th International Conference on Natural Computation, 2014, pp. 192–196.

BPS, Indeks Harga Konsumen dan Inflasi DKI Jakarta 2015. DKI Jakarta: Badan Pusat Statistik, 2015.

BPS Provinsi Jawa Tengah, Indeks Harga Konsumen dan Inflasi Provinsi Jawa Tengah 2019. Semarang: BPS Provinsi Jawa Tengah, 2019.

L. Irawati, Tarno, and H. Yasin, “Peramalan Indeks Harga Konsumen 4 Kota di Jawa Tengah Menggunakan Model Generalized Space Time Autoregressive (GSTAR),” J. Gaussian, vol. 4, no. 3, pp. 553–562, 2015.

M. F. Andriyani, A. Hoyyi, and H. Yasin, “Pemodelan Indeks Harga Konsumen di Jawa Tengah dengan Metode Generalized Space Time Autoregressive Seemingly Unrelated Regression (GSTAR-SUR),” J. Gaussian, vol. 7, no. 4, pp. 337–347, 2018.

BPS Provinsi Jawa Tengah, Indeks Harga Konsumen dan Inflasi Provinsi Jawa Tengah 2019. Badan Pusat Statistik Provinsi Jawa Tengah, 2020.

M. Alawiyah, D. A. Kusuma, and B. N. Ruchjana, “Model Space Time Autoregressive Integrated (STARI) untuk Data Debit Air Sungai Citarum di Provinsi Jawa Barat,” BAREKENG J. Ilmu Mat. dan Terap., vol. 14, no. 1, pp. 147–158, 2020.

F. Kristianda and K. Fithriasari, “Peramalan Curah Hujan di Wilayah Surabaya Timur dengan Vector Autoregressive Neural Network,” J. Sains dan Seni, vol. 5, no. 2, pp. 2337–3520, 2016.

H. Bonar, B. Nurani Ruchjana, and G. Darmawan, “Development of Generalized Space Time Autoregressive Integrated with ARCH error ( GSTARI – ARCH ) Model Based on Consumer Price Index Phenomenon at Several Cities in North Sumatera Province,” in 2nd International Conference on Applied Statistics (ICAS II), 2016, 2017, doi: 10.1063/1.4979425.

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.

M. S. Akbar, “A Generalized Space-Time Autoregressive Moving Average ( GSTARMA ) Model for Forecasting Air Pollutant in Surabaya,” in ICoMPAC 2019, 2020, pp. 1–8, doi: 10.1088/1742-6596/1490/1/012022.

P. E. Pfeifer and S. J. Deutsch, “A Three-Stage Iterative Procedure for Space-Time Modeling,” Technometrics, vol. 22, no. 1, pp. 35–47, 1980.

A. D. Sulistyono, Hartawati, N. W. Suryawardhani, A. Iriany, and A. Iriany, “Cross-Covariance Weight of GSTAR-SUR Model for Rainfall Forecasting in Agricultural Areas,” CAUCHY –Jurnal Mat. Murni dan Apl., vol. 6, no. 2, pp. 49–57, 2020.

D. Astuti, B. N. Ruchjana, and Soemartini, “Generalized space time autoregressive with exogenous variable model and its application,” in Journal of Physics: Conference Series, 2017, vol. 893, no. 1, doi: 10.1088/1742-6596/893/1/012038.

D. Rosyida, A. Iriany, and Nurjannah, “GSTAR-X-SUR Model with Neural Network Approach on Residuals,” CAUCHY –Jurnal Mat. Murni dan Apl., vol. 5, no. 4, pp. 203–210, 2019.

Setiawan, Suhartono, and M. Prastuti, “S-GSTAR-SUR Model for Seasonal Spatio Temporal Data Forecasting,” Malaysian J. Math. Sci., vol. 10, pp. 53–65, 2016.

G. Habibie, Yundari, and H. Perdana, “Penerapan GSTAR-SUR pada Jumlah Penumpang Pesawat Domestik di Bandara Indonesia,” Bul. Ilm. Math. Stat. dan Ter., vol. 09, no. 2, pp. 275–284, 2020.

W. P. . Septyaningrum, “Peramalan Jumlah Wisatawan Pada Tiga Lokasi Wisata Di Kabupaten Pacitan Menggunakan Metode GSTAR-SUR,” 2017.

N. R. Sari, W. F. Mahmudy, A. P. Wibawa, and U. Brawijaya, “The Effectiveness of Hybrid Backpropagation Neural Network Model and TSK Fuzzy Inference System for Inflation Forecasting,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 2, pp. 111–117, 2017.

H. Hassani and D. Thomakos, “A review on singular spectrum analysis for economic and financial time series,” Stat. Interface, vol. 3, no. 3, pp. 377–397, 2010, doi: 10.4310/sii.2010.v3.n3.a11.

S. Harini and N. Nuronia, “Determination of Consumer Price Index with Generalized Space-Time Autoregressive,” in ICGT 2019, 2020, p. 456, doi: 10.1088/1755-1315/456/1/012076.

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.

M. R. Susila, “Spatio-Temporal Analysis of Rupiah Loans Provided by Commercial Banks and Rural Banks,” BAREKENG J. Math. Its Appl., vol. 16, no. 3, pp. 1003–1012, 2022.

Published
2023-06-11
How to Cite
[1]
P. Arum, A. Indriani, and M. Haris, “FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME”, BAREKENG: J. Math. & App., vol. 17, no. 2, pp. 1183-1192, Jun. 2023.
Issue
Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Prizka Rismawati Arum, Anita Retno Indriani, M Al Haris

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