COMPARING NEURAL NETWORK AUTOREGRESSIVE METHOD FOR IMPORT DUTY REVENUE FORECASTING
DOI:
https://doi.org/10.31092/jpbc.v6i1.1560Abstract
Neural Network is one of the interesting methods in data analytics used to forecast time-series data in the previous years. In one of the initiative strategies, the Directorate General of Customs and Excise (DGCE) compares this method with Holt-Winters exponential smoothing to get more accurate forecasting of import duty revenue. This paper compares Holt-Winters and Neural Network(NN) to get better forecasting for import duty revenue using data from Customs and Excise Information System Automation (CEISA) billing system. As a result, NN gives a better result with a lower Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Therefore, neural networks should be used to forecast and monitor the realization of import duty revenue so DGCE can identify the change in economic indicators that can affect import duty revenue earlier and produce the right policy to respond to it.
References
Benrhmach, G., Namir, K., Namir, A., & Bouyaghroumni, J. (2020). Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series. Journal of Applied Mathematics, 2020, 1–6. https://doi.org/10.1155/2020/5057801
da Silva, L. C., da Costa, K., Canas Rodrigues, P., Salas, R., & López-Gonzales, J. L. (2022). Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector. Energies, 15(2), 588. https://doi.org/10.3390/en15020588
Horák, J., & Krulický, T. (2019). Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company. SHS Web of Conferences, 61, 01006. https://doi.org/10.1051/shsconf/20196101006
Kotsialos, A., Papageorgiou, M., & Poulimenos, A. (2005). Holt-Winters and Neural-Network Methods for Medium-Term Sales Forecasting. IFAC Proceedings Volumes, 38(1), 133–138. https://doi.org/10.3182/20050703-6-CZ-1902.01506.
Maleki, A., Nasseri, S., Aminabad, M. S., & Hadi, M. (2018). Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics. KSCE Journal of Civil Engineering, 22(9), 3233–3245. https://doi.org/10.1007/s12205-018-1195-z
Martyniuk, V., Wolowiec, T., & Mieszajkina, E. (2021). Planning and Forecasting Customs Revenues to the State Budget: A Case Study of Ukraine. EUROPEAN RESEARCH STUDIES JOURNAL, XXIV(Special Issue 2), 648–665. https://doi.org/10.35808/ersj/2301
Nyoni, T. (2019). Exports and Imports in Zimbabwe: Recent Insights from Artificial Neural Networks. 15.
Purwana, A. S. (2019). Liberalisasi Perdagangan dan Penerimaan Kepabeanan Impor. Jurnal Perspektif Bea dan Cukai, 3(2). https://doi.org/10.31092/jpbc.v3i2.555
Saba, A. I., & Elsheikh, A. (2020). Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks. Process Safety and Environmental Protection, 8.
Sharadqah, S., M, A., A, M., Marbello, R., & Mercedes, S. (2021). Nonlinear Rainfall Yearly Prediction based on Autoregressive Artificial Neural Networks Model in Central Jordan using Data Records: 1938-2018. International Journal of Advanced Computer Science and Applications, 12(2). https://doi.org/10.14569/IJACSA.2021.0120231
Sun, S., Lu, H., Tsui, K.-L., & Wang, S. (2019). Nonlinear vector auto-regression neural network for forecasting air passenger flow. Journal of Air Transport Management, 78, 54–62. https://doi.org/10.1016/j.jairtraman.2019.04.005
Karadzic, V., & Pejovic, B. (2021). Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models. Www.Amfiteatrueconomic.Ro, 23(57), 517. https://doi.org/10.24818/EA/2021/57/517
Wang, L., Wang, Z., Qu, H., & Liu, S. (2018). Optimal Forecast Combination Based on Neural Networks for Time Series Forecasting. Applied Soft Computing, 66, 1–17. https://doi.org/10.1016/j.asoc.2018.02.004
Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal
of Operational Research, 160(2), 501–514. https://doi.org/10.1016/j.ejor.2003.08.037
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
