Luchko, M.R., Dziubanovska, N. and Arzamasova, O. (2021) Artificial Neural Networks in Export and Import Forecasting: An Analysis of Opportunities. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Cracow, 22-25 September 2021, 916-923.
https://doi.org/10.1109/IDAACS53288.2021.9660856

has been cited by the following article:

• TITLE: Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports

  AUTHORS: Mohamed A. H. Milad, Hanan Moh. B. Duzan

  KEYWORDS: Composite Model, UECM, ARIMA, Forecasting, Malaysia

  JOURNAL NAME: Open Journal of Statistics, Vol.14 No.2, March 29, 2024

  ABSTRACT: For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.