Evaluation of the performance of TBATS and SARIMA methods in forecasting air temperature in Indonesia
DOI:
https://doi.org/10.30606/absis.v8i2.3052Keywords:
forecasting, seasonal patterns, Indonesian air temperature, SARIMA, TBATS, air temperature forecasting, model performance evaluationAbstract
Indonesia is a tropical archipelago located along the equator, where air temperature patterns exhibit seasonal trends and unstable fluctuations. This instability can impact several sectors, including agriculture—making it difficult for farmers to determine planting and harvesting times—electricity demand, which increases during hotter periods, and public health, as erratic weather may reduce productivity and elevate the risk of diseases such as dehydration, asthma, and respiratory infections. This study aims to evaluate the performance of the Trigonometric, Box-Cox Transformation, ARMA Errors, Trend, and Seasonal (TBATS) model and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in forecasting air temperature in Indonesia. The dataset used comprises 2-meter air temperature records in Indonesia from January 1940 to August 2024, obtained from ECMWF. The evaluation method applied is cross-validation with a rolling basis. The results show that the RMSE for the TBATS model is 21.3843%, while the SARIMA model has an RMSE of 21.2958%. These results indicate that SARIMA has a slightly better performance than TBATS. However, both methods perform well in forecasting air temperature in Indonesia, as their RMSE percentages are within an acceptable range. This research is expected to contribute to the scientific literature on air temperature forecasting in Indonesia and encourage further studies on hybrid models that integrate TBATS and SARIMA. Additionally, it may support efforts to mitigate the adverse impacts of air temperature changes in the country.
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References
Adityo Wicaksono. (2024, November 18). Anomali suhu udara bulan Agustus 2024. Badan Meteorologi Klimatologi dan Geofisika (BMKG). https://www.bmkg.go.id/berita/?p=anomali-suhu-udara-bulan-agustus-2024&lang=ID&tag=anomali-suhu-udara-bulanan
Agustin, A., Rahani, F. F., & Indikawati, F. I. (2022). Prediksi kualitas air menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Jurnal Manajemen Informatika (JAMIKA), 12(2), 137–150. https://doi.org/10.34010/jamika.v12i2.8022
Aprianti, A., Faulina, N., & Usman, M. (2024). Generalized Space Time Autoregressive (GSTAR) model for air temperature forecasting in the South Sumatera, Riau, and Jambi Provinces. InPrime: Indonesian Journal of Pure and Applied Mathematics, 6(1), 1–13. https://doi.org/10.15408/inprime.v6i1.36049
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771
Fajar, M., & Nonalisa, S. (2021). Forecasting chili prices using TBATS. International Journal of Scientific Research in Multidisciplinary Studies, 7(2), 1–5.
Guo, Z., Gong, H., Li, Y., Tao, C., & LuoJing, Z. (2022). Time series analysis and prediction of scarlet fever incidence trends in Jiangsu Province, China: Using ARIMA and TBATS models. Research Square, 1–18. https://doi.org/10.21203/rs.3.rs-2259096/v1
Hutagalung, A. A., & Sari, R. F. (2024). Forecasting Indihome users by using Trigonometrics, Box Cox, Transformation, Arma Error, Trend, and Seasonal (TBATS) methods. Mathline: Jurnal Matematika dan Pendidikan Matematika, 9(1), 243–256. https://doi.org/10.31943/mathline.v9i1.579
Jaya, I. G. M., & Chadidjah, A. (2020). Modeling and forecasting of the temperature in Bandung, Indonesia: The next ten years. International Journal of Innovative Science, Engineering and Technology (IJISET), 7(7), 19–27.
Kafara, Z., Rumlawang, F., & Sinay, L. (2017). Peramalan curah hujan dengan pendekatan Seasonal Autoregressive Integrated Moving Average (SARIMA): Studi kasus curah hujan bulanan di Kota Ambon, Provinsi Maluku. Barekeng: Jurnal Ilmu Matematika dan Terapan, 11(1), 63–74. https://doi.org/10.30598/barekengvol11iss1pp63-74
Karabiber, O. A., & Xydis, G. (2019). Electricity price forecasting in the Danish day-ahead market using the TBATS, ANN and ARIMA methods. Energies, 12(5), Article 928. https://doi.org/10.3390/en12050928
Kusumawati, F. A., & Kuswanto, H. (2014). Pemodelan beban sistem listrik Jawa-Bali dengan menggunakan pendekatan flexible seasonality forecasting. Prosiding Seminar Nasional Matematika Universitas Jember, 1(1), 121–131.
Maulana, H. A., Muliah, Sampe, M. Z., & Hanifah, F. (2017). Pemodelan dan peramalan deret waktu: Studi kasus suhu permukaan laut di selatan Jawa Timur. Math Educa Journal, 1(2), 187–199.
Munim, Z. H. (2022). State-space TBATS model for container freight rate forecasting with improved accuracy. Maritime Transport Research, 3, Article 100057. https://doi.org/10.1016/j.martra.2022.100057
Salsabila, S., Kusuma, D. A., & Ruchjana, B. N. (2022). Penerapan model Vector Autoregressive orde tiga pada data suhu udara rata-rata di Kabupaten Malang dan Kabupaten Sidoarjo. Teorema: Teori dan Riset Matematika, 7(2), 247–254. https://doi.org/10.25157/teorema.v7i2.7200
Shrivastava, S. (2020, December 1). Cross-validation in time series. Medium. https://medium.com/@soumyachess1496/cross-validation-in-time-series-566ae4981ce4
Solihat, F. L. N., Putri, F. A., Lastiar, N., & Pontoh, R. S. (2022). Analisis komparatif peramalan suhu rata-rata Palembang menggunakan ARIMA, SARIMA, dan NNAR. Seminar Nasional Statistika Aktuaria I.
Susanti, L., Hasanah, P., & Winarni, W. (2020). Peramalan suhu udara dan dampaknya terhadap konsumsi energi listrik di Kalimantan Timur. Barekeng: Jurnal Ilmu Matematika dan Terapan, 14(3), 399–412. https://doi.org/10.30598/barekengvol14iss3pp399-412
Wolff, N. H., Zeppetello, L. R. V., Parsons, L. A., Aggraeni, I., Battisti, D. S., Ebi, K. L., ... & Spector, J. T. (2021). The effect of deforestation and climate change on all-cause mortality and unsafe work conditions due to heat exposure in Berau, Indonesia: A modelling study. The Lancet Planetary Health, 5(12), e882–e892. https://doi.org/10.1016/S2542-5196(21)00279-5
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