Modeling and forecasting bitumen sales using univariate and multivariate time series approaches: a case study of Algeria's NAFTAL corporation

Youssouf  Bouzir; Mouhamadou Djima  Baranon; Mohamed Adam  Suliman Ishag; Michael Arthur  Ofori; Daniel Biftu Bekalo

doi:10.34118/djei.v17i2.4666

Youssouf Bouzir The Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), (Kenya) http://orcid.org/0000-0002-8831-4244
Mouhamadou Djima Baranon The Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), (Kenya) http://orcid.org/0009-0005-7783-653X
Mohamed Adam Suliman Ishag University of Kordofan (Sudan) http://orcid.org/0000-0002-7939-5097
Michael Arthur Ofori University of Cape Coast, Cape Coast (Ghana) http://orcid.org/0000-0002-4983-540X
Daniel Biftu Bekalo Haramaya University, Addis Ababa (Ethiopia) http://orcid.org/0000-0002-7935-2409

DOI: https://doi.org/10.34118/djei.v17i2.4666

Keywords: Bitumen, Box-Jenkins, SARIMA, ARCH-LM, VAR

Abstract

This study models and forecasts the NAFTAL Corporation’s bitumen sales in Algeria using univariate and multivariate time series methods. This method uses the Box-Jenkins method to identify, estimate, diagnostic and predict. ANOVA and ADF tests showed seasonal patterns and stationary data. The best univariate models for bitumen (BTM) and pure bitumen (BTMP) were found to be SARIMAX(2,2,1) and SARIMAX(0,1,4), respectively. No volatility clustering or ARCH effects were captured via ARCH-LM tests. VAR(2) model used to explain dynamic interactions over time. Granger causality tests detected a significant unidirectional effect between BTM and BTMP (p < 0.001 at lag 2). According to the forecast error variance decomposition (FEVD), BTM accounts for 29.44% of the 12-month forecast error variance for BTMP and 98.25% of its own variation. These findings emphasize how crucial it is to take into account the overall bitumen trends when estimating product demand.

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