PENGEMBANGAN MODEL PERAMALAN SPACE TIME
Studi Kasus: Data Produksi Padi di Sulawesi Selatan
Keywords:forecasting, gstarimax, rainfall, rice production, sarimax
Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.
Andayani, N., Sumertajaya, I., Ruchjana, B., & Aidi, M. (2018). Comparison of GSTARIMA and GSTARIMA-X Model by Using Transfer Function Model Approach to Rice Price Data. The 4th International Seminar on Sciences. IOP Conference Series: Earth and Environment Science 187. Presented at the Bogor (ID). Bogor (ID): Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (FMIPA IPB).
Borovkova, S., Lopuhaa, H., & Ruchjana, B. (2002). Generalized STAR model with experimental weights. Proceedings of the 17th International Workshop on Statistical Modelling, 139–147. Chania, Greece.
[BPS] Badan Pusat Statistik. (2016). Produksi Tanaman Pangan 2016. Jakarta (ID): Badan Pusat Statistik.
Cryer, J. (1986). Time Series Analysis. Boston: PWS-KENT Publishing Company.
Ishaq, M., Rumiati, A., & Permatasari, E. (2016). Analisis Faktor-Faktor yang Mempengaruhi Produksi Padi di Provinsi Jawa Timur Menggunakan Regresi Semiparametrik Spline. Jurnal Sains Dan Seni ITS, 5: 2337–3520.
Lesage, J., & Pace, K. (2009). Introduction to Spastial Econometrics. Boca Raton (US): CRC Press/Taylor and Francis.
Makridakis, S., Wheelwright, S., & Mcgee, V. (1999). Metode dan Aplikasi Peramalan. Jakarta (ID): Erlangga.
Montgomery, D., Jennings, C., & Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting. Canada: John Wiley&Sons, Inc.
Qomariyah, L., Toharudin, T., & Soemartini, S. (2017). Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model to Forecast Cocoa Export Volume. Proceeding The International Conference on Applied Statistics, 2(1), 51–58. Bandung (ID): Departemen Statistika FMIPA Universitas Padjadjaran.
Rochayati, I., Syafitri, U. D., & Sumertajaya, I. M. (2019). Kajian model peramalan kunjungan wisatawan mancanegara di bandara kualanamu medan tanpa dan dengan kovariat. Indonesian Journal of Statistics and Its Applications, 3(1): 18–32.