Forecasting Indonesia's Export Values Using SETAR-GA and SETAR-Tree
Received: 4 May 2025 | Revised: 7 June 2025 and 10 July 2025 | Accepted: 11 July 2025 | Online: 6 October 2025
Corresponding author: Heri Kuswanto
Abstract
This study employs two advanced nonlinear time series models, SETAR-GA and SETAR-Tree, to forecast Indonesia's export values. The SETAR-GA model integrates a genetic algorithm to optimize parameters within a self-exciting threshold autoregressive framework, while the SETAR-Tree model combines SETAR with a recursive partitioning approach to capture regime changes more flexibly. The nonlinearity in the export data was confirmed using the Terasvirta test. The performance of both models is evaluated using in-sample and out-of-sample forecasting accuracy, assessed through MAPE and RMSE. The results indicate that both models are capable of capturing nonlinear patterns in export data, with SETAR-GA showing superior forecasting performance. These findings highlight the potential of nonlinear models to improve export forecasting in emerging economies.
Keywords:
genetic algorithm, forecasting, nonlinear, SETAR-Tree, accuracyDownloads
References
F. Zhou, H. Wen, and C. C. Lee, "Broadband infrastructure and export growth," Telecommunications Policy, vol. 46, no. 5, Jun. 2022, Art. no. 102347.
E. Dhyne, A. K. Kikkawa, M. Mogstad, and F. Tintelnot, "Trade and Domestic Production Networks," The Review of Economic Studies, vol. 88, no. 2, pp. 643–668, Mar. 2021.
S. R. Ajija, A. F. Zakia, and R. Purwono, "The impact of opening the export promotion agencies on Indonesia’s non-oil and gas exports," Heliyon, vol. 7, no. 8, Aug. 2021, Art. no. e07756.
N. Norouzi, "Post-COVID‐19 and globalization of oil and natural gas trade: Challenges, opportunities, lessons, regulations, and strategies," International Journal of Energy Research, vol. 45, no. 10, pp. 14338–14356, Aug. 2021.
R. Waheed, S. Sarwar, and A. Dignah, "The role of non-oil exports, tourism and renewable energy to achieve sustainable economic growth: What we learn from the experience of Saudi Arabia," Structural Change and Economic Dynamics, vol. 55, pp. 49–58, Dec. 2020.
R. S. Tsay and R. Chen, Nonlinear Time Series Analysis. John Wiley & Sons, 2018.
H. Tong, Non-linear Time Series: A Dynamical System Approach. Oxford University Press, 1990.
K. Deb, "An introduction to genetic algorithms," Sadhana, vol. 24, no. 4–5, pp. 293–315, Aug. 1999.
T. Putri and I. Irhamah, "Peramalan Return Saham Bank Central Asia Menggunakan Self Exciting Threshold Autoregressive–Genetic Algorithm," Jurnal Sains dan Seni ITS, vol. 5, no. 1, pp. D71–D76, 2016.
M. Nurhidayati and I. Irhamah, "Identification of Self-Exciting Threshold Autoregressive Model by Using Genetic Algorithm," presented at the 5th Annual Basic Science International Conference, Malang, 2015, pp. 271–273.
A. F. Ashill, "Harga Emas Indonesia Menggunakan SETAR dan SETAR-Tree," M.S. Thesis, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, 2023.
W. S. Chan, A. C. S. Wong, and H. Tong, "Some Nonlinear Threshold Autoregressive Time Series Models for Actuarial Use," North American Actuarial Journal, vol. 8, no. 4, pp. 37–61, Oct. 2004.
M. A. Albadr, S. Tiun, M. Ayob, and F. AL-Dhief, "Genetic Algorithm Based on Natural Selection Theory for Optimization Problems," Symmetry, vol. 12, no. 11, Oct. 2020, Art. no. 1758.
K, Zhang, J. Lei, Q. Zhou, and Y. Wang, "Using Genetic Algorithm to Optimize Parameters of Support Vector Machine and Its Application in Material Fatigue Life Prediction," Advances in Natural Science, vol. 8, no. 1, pp. 21–26, 2015.
R. Godahewa, G. I. Webb, D. Schmidt, and C. Bergmeir, "SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting," Machine Learning, vol. 112, no. 7, pp. 2555–2591, Jul. 2023.
K. Chan, "Limiting properties of the least squares estimator of a continuous threshold autoregressive model," Biometrika, vol. 85, no. 2, pp. 413–426, Jun. 1998.
F. T. Kristanti, M. Y. Febrianta, D. F. Salim, H. A. Riyadh, and B. A. H. Beshr, "Predicting Financial Distress in Indonesian Companies using Machine Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 17644–17649, Dec. 2024.
J. Bouslimi, S. Boubaker, and K. Tissaoui, "Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14162–14169, Jun. 2024.
Y. Sasmita, H. Kuswanto, and D. D. Prastyo, "State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export," Forecasting, vol. 6, no. 1, pp. 152–169, Feb. 2024.
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Copyright (c) 2025 Heri Kuswanto, Yunus Iman Kattaba, Hidayatul Husna, Irhamah, Kartika Fithriasari
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