Enhancing the ARIMAX model by using the bivariate wavelet denoising: Application on road traffic accidents

Section: Research Paper
Issue
Vol. 20 No. 2 (2023): Volume 20 Issue 2
Published
Jun 25, 2025
Pages
15-29

Abstract

The purpose of this study is to determine whether an enhanced confound model representing bivariate wavelet-autoregressive integrated moving average with exogenous variable BWARIMAX is beneficial for predicting monthly traffic accidents. A wavelet-based multiresolution analysis MRA, conducted before the ARIMAX model fitting, shows that the performance of ARIMAX models in predicting traffic accidents can be significantly improved. The method described in this study identifies the ideal wavelet function, wavelet transform, and number of decomposition levels for the MRA and consequently considerably improves forecast accuracy. The analysis of the study demonstrated the superiority of the suggested approach and revealed that utilizing the BWARIMAX method, we can extract more information from the series, which enhances the performance of the original ARIMAX model in terms of predicting. Additionally, it has been demonstrated through extensive empirical testing using a wide range of wavelet families that Daubechies and Coiflet wavelets are excellent choices for denoising data. Furthermore, the study concluded that out of the two wavelet families, the performance of the Coiflet wavelet of order 3 was better

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Authors

nawroz mikaeel Ahmed

Statistics Department-College of Administration and Economic-University of Duhok, Duhok, Iraq

ORCID
Qais Mustafa Abdulqader

Petroleum Geology Department–Technical College of Petroleum and Mineral Science-Duhok Polytechnic University, Duhok, Iraq

ORCID

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How to Cite

mikaeel Ahmed, nawroz, نوروز, Mustafa Abdulqader, Q., & قیس. (2025). Enhancing the ARIMAX model by using the bivariate wavelet denoising: Application on road traffic accidents. IRAQI JOURNAL OF STATISTICAL SCIENCES, 20(2), 15–29. Retrieved from https://rjps.uomosul.edu.iq/index.php/stats/article/view/20649