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Abstract
This research presents a comparison of the performance of three forecasting methods, namely ARIMA (Box Jenkins), Multiscale Autoregressive (MAR), and Singular Spectrum Analysis (SSA), in dealing with non-stationary export data challenges. The focus of the study is on forecasting the export value of Bengkulu Province FOB (Free on Board) Pelabuhan Baai from January 2019 to September 2023. By using ARIMA as a classical approach, MAR and SSA as representations of multiscale and signal decomposition approaches, this study aims to provide a comprehensive understanding of the effectiveness of each method in dealing with dynamic export data characteristics. Performance evaluation is carried out using criteria such as Mean Absolute Percentage Error (MAPE), with the hope of providing valuable insights for selecting the optimal forecasting method in the context of Bengkulu Province's exports.
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Copyright (c) 2024 Ahmad Agil Shidigie, Laveni Yurike, Della Puspita, Aura Julieta, Nurul Hidayati, Meli Handayani Catur Putri
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
- R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice (2nd ed.), Otexts, 2018.
- P. J. Brockwell and R. A. Davis, Introduction to time series and forecasting (2nd ed.), Springer, 2002.
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- I. Daubechies, Ten Lectures On Wavelest, Philadelphia: Society for Industrial and Applied Mathematics, 1992.
- Irwan, A. Sauddin, and A. Kaimuddin, “Proyeksi Produksi Padi Kabupaten Pinrang dengan Metode Singular Spectrum Analysis”, Jurnal Matematika dan Statistika serta Aplikasinya (JMSA), Vol. 10, No. 1, 2022.
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References
R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice (2nd ed.), Otexts, 2018.
P. J. Brockwell and R. A. Davis, Introduction to time series and forecasting (2nd ed.), Springer, 2002.
A. M. Sainah. “Akurasi Peramalan Long Horizon dengan Singular Spectrum Analysis”, Jurnal of Sunan gunung Jati StateIslmaic University (UIN), Vol. 3, No. 2, 2018
Undang-Undang Kepabeanan Nomor 17 Tahun 2006
”Badan Pusat Statistik Kota Bengkulu”, 2023. [Online]. Available: https://bengkulukota.bps.go.id/
G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. Third Edition. New Jersey: Prentice Hall, 1994.
J. D. Cryer and K. S. Chan, Time Series Analysis with Application in R. Second Edition, USA: Spinger Science dan Business Media, 2008.
O. Renaud, J. L. Starck, and F. Murtagh, “Prediction Based o n a Multiscale Decomposition”, International Journal of Wavelets, Multiresolution and Information Processing, Vol.1, No. 2, 2003
Makridakis, dkk, Metode dan Aplikasi Peramalan, Jakarta: Erlangga, 1999.
I. Daubechies, Ten Lectures On Wavelest, Philadelphia: Society for Industrial and Applied Mathematics, 1992.
Irwan, A. Sauddin, and A. Kaimuddin, “Proyeksi Produksi Padi Kabupaten Pinrang dengan Metode Singular Spectrum Analysis”, Jurnal Matematika dan Statistika serta Aplikasinya (JMSA), Vol. 10, No. 1, 2022.
E. Purnama, “Aplikasi Metode Singular Spectrum Analysis (SSA) Pada Peramalan Curah Hujan Di Provinsi Gorontalo”, Jambura Journal Of Probability And Statistics, Vol. 3, No. 2, 2022.
R. Idrus, Ruliana, and Aswi, ”Penerapan Metode Singular Spectrum Analysis dalam Peramalan Jumlah Produksi Beras di Kabupaten Gowa”, VARIANSI: Journal of Statistics and Its Application on Teaching and Research, Vol. 4, No. 2, 2022.
H. Khaeri, E. Yulian, and Darmawan, “Penerapan Metode Singular Spectrum Analysis (SSA) Pada Peramalan Jumlah Penumpang Kereta Api Di Indonesia Tahun 2017”, Jurnal Euclid, Vol. 5, No. 1, 2018.
Golyandina and Zhigljavsky, Singular Spectrum Analysis for Time Series, STAT ME, 2013.