Forecasting Non-Oil and Gas Exports in Indonesia Using Double and Triple Exponential Smoothing Methods

Authors

  • Bustami Universitas Riau, Pekanbaru
  • Anne Mudya Yolanda Universitas Riau, Pekanbaru
  • Nisha Thahira Universitas Riau, Pekanbaru

DOI:

https://doi.org/10.24002/ijieem.v5i1.6211

Keywords:

forecasting, , non-oil gas exports, Holt-Winters, multiplicative Holt-Winters, additives Holt-Winters

Abstract

Non-oil and gas exports could be forecasted using exponential smoothing for future periods. This study examines non-oil and gas export data in Indonesia from January 2015 to May 2021, indicating trends and seasonality. Based on the data characteristics, the obtained data were analyzed using Holt's double exponential smoothing method and triple exponential smoothing with multiplicative and additives Holt-Winters. The MAPE for all three models is less than 10%, indicating that the method is very good and could be used to forecast the next period. Using MAPE as a comparison, the best model for non-oil and gas exports is the additive Holt-Winters method triple exponential smoothing, which has the lowest MAPE of any model. The best method was employed to forecast data, making it possible for us to anticipate the pattern of non-oil and gas exports. This forecast data could be used as the basis for policymakers' decision-making. The forecast results using this method indicate that the value of non-oil exports will increase for the next period.

References

Ahmar, A. S., Fitmayanti, F., & Ruliana, R. (2022). Modeling inflation cases in South Sulawesi Province using single exponential smoothing and double exponential smoothing methods. Quality and Quantity, 56(1), 227–237.

Andriani, N., Wahyuningsih, S., & Siringoringo, M. (2022). Application of Double Exponential Smoothing Holt and Triple Exponential Smoothing Holt-Winter with Golden Section Optimization to Forecast Export Value of East Borneo Province. Jurnal Matematika, Statistika, & Komputasi, 18(3), 475–483.

Badan Pusat Statistik. (2021). Perkembangan Ekspor dan Impor Indonesia Mei 2021 (Issue 44).

Badan Pusat Statistik. (December, 2022). Perkembangan Ekspor dan Impor Indonesia (Issue 05).

Chang, P. C., Wang, Y. W., & Liu, C. H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1), 1–11.

Chatfield, C., & Xing, H. (2019). The analysis of time series: An introduction with R (7th ed). CRC Press.

Cryer, J. D., & Chan, K.-S. (2008). Time Series Analysis with Application in R (2nd ed). Springer.

Emmanuel, O. O., Adebanji, A., & Labeodan, O. (2014). Using Holt Winter’s Multiplicative Model to Forecast Assisted Childbirths at the Teaching Hospital in Ashanti Region, Ghana. Journal of Biology, Agriculture and Healthcare, 4(9), 83-88.

Hoarau, M. (2022). Time Series Analysis on AWS. Packt Publishing.

Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: Principles and practice. OTexts.

Illukkumbura, A. (2021). Introduction to Time Series Analysis. Anusha Books

Kementerian Perindustrian Republik Indonesia. (2021). Laporan Informasi 2021. Kementerian Perindustrian Republik Indonesia. https://kemenperin.go.id/download/27418/Laporan-Informasi-Industri-2021

Makridakis, S., Wheelwright, S. C., & McGee, V. E. (1999). Metode dan aplikasi peramalan. Erlangga.

Mills, T. C. (2019). Applied time series analysis. Elsevier.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting. Wiley.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (2nd ed). Wiley.

Nielsen, A. (2020). Practical Time Series: Prediction with Statistics & Machine Learning. O’Reilly.

Ramadania, R. (2018). Peramalan harga beras bulanan di tingkat penggilingan dengan metode weighted moving average. Buletin Ilmiah Matematika, Statistika Dan Terapannya (Bimaster), 7(4), 329-334.

Ribeiro, R. C. M. (2019). Holt-Winters Forecasting for Brazilian Natural Gas Production. International Journal of Innovation Education and Research, 7(6), 119-129.

Rosadi, D. (2014). Analisis runtun waktu dan aplikasinya dengan R. UGM Pres.

Siregar, B., Butar-Butar, I. A., Rahmat, R. F., Andayani, U., & Fahmi, F. (2017). Comparison of Exponential Smoothing Methods in Forecasting Palm Oil Real Production. Journal of Physics: Conference Series, 801, Article 012004.

Wei, W. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods (2nd ed). Pearson.

Woodward, W. A., Sadler, B. P., & Robertson, S. D. (2015). Time Series for Data Science. CRC Press.

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Published

2023-06-30

How to Cite

Bustami, Yolanda, A. M., & Thahira, N. . (2023). Forecasting Non-Oil and Gas Exports in Indonesia Using Double and Triple Exponential Smoothing Methods. International Journal of Industrial Engineering and Engineering Management, 5(1), 45–49. https://doi.org/10.24002/ijieem.v5i1.6211

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