Short run forecasting of petroleum prices in Tanzania using ARIMA and exponential smoothing: A comparative analysis

Authors

  • Bahati Ilembo Department of Mathematics and Statistics, Mzumbe University, P.O. Box 1, Morogoro, Tanzania Author https://orcid.org/0000-0001-5991-2571
  • Sharifu Nurdin Department of Mathematics and Statistics, Mzumbe University, P.O. Box 1, Morogoro, Tanzania Author

DOI:

https://doi.org/10.51867/asarev.2.1.7

Keywords:

Akaike Information Criteria, ARIMA, Bayesian, Ljung–Box

Abstract

Tanzania's economy remains highly dependent on imported petroleum, with petrol price fluctuations significantly impacting inflation, transportation costs, and household welfare. The purpose of the study was to conduct a comparative analysis of ARIMA and Exponential Smoothing methods for short-run forecasting of petrol prices in Tanzania to identify the most accurate model for predicting future petroleum price trends. Using monthly petrol price data from 2005 to 2024 obtained from the Bank of Tanzania (BOT), we applied the Box-Jenkins methodology to compare the Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing models. The ARIMA (1,1,4) with drift model was identified as optimal based on Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and maximum log-likelihood criteria. This model achieved superior forecasting accuracy with a Mean Absolute Percentage Error (MAPE) of 2.44%, compared to 2.58% for Exponential Smoothing. The model forecasts a steady monthly increase of 0.3-0.5% in petrol prices, projecting prices to reach 3,500.65 TZS/L by September 2026. While the model demonstrates strong predictive performance (Ljung-Box p-value = 0.265), its limitations in anticipating sudden price shocks highlight the need for complementary risk management strategies. These findings provide policymakers and market participants with a reliable tool for budget planning and economic forecasting. The study underscores Tanzania's vulnerability to global oil market volatility and emphasizes the importance of developing strategic fuel reserves and alternative energy sources to enhance energy security.

References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.

https://doi.org/10.1109/TAC.1974.1100705 DOI: https://doi.org/10.1109/TAC.1974.1100705

Bank of Tanzania [BOT]. (2024). Annual economic report 2024: Fuel imports and inflation trends. Dar es Salaam: BOT Publications.

Baumeister, C., & Hamilton, J. D. (2019). Structural interpretation of vector Autoregression with incomplete identification: Revisiting the role of oil supply and demand shocks. American Economic Review, 109(5), 1873-1910. https://doi.org/10.1257/aer.20151569 DOI: https://doi.org/10.1257/aer.20151569

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco, CA: Holden-Day.

Cologni, A., & Manera, M. (2008). Oil prices, inflation, and interest rates in a structural cointegrated VAR model for the G-7 countries. Energy Economics, 30(3), 856-888. https://doi.org/10.1016/j.eneco.2007.02.003 DOI: https://doi.org/10.1016/j.eneco.2006.11.001

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.

https://doi.org/10.2307/2286348 DOI: https://doi.org/10.2307/2286348

https://doi.org/10.1080/01621459.1979.10482531 DOI: https://doi.org/10.1080/01621459.1979.10482531

Dimitrov, B. (2008). Exponential smoothing for time series forecasting. Journal of Applied Statistics, 35(6), 567-580.

Energy and Water Utilities Regulatory Authority [EWURA]. (2024). Quarterly fuel price report 2024. Dar es Salaam: EWURA Publications.

Eze, C., & Onyema, J. (2024). The impact of petrol price increases on household welfare in Nigeria. Journal of African Economics, 33(2), 145-160.

Gelan, A. (2018). The impact of oil price changes on inflation in Sub-Saharan Africa: Evidence from panel cointegration analysis. Energy Economics, 70, 324-333. https://doi.org/10.1016/j.eneco.2018.01.018 DOI: https://doi.org/10.1016/j.eneco.2018.01.018

Hamilton, J. D. (2019). Oil prices and the macro economy. Handbook of Macroeconomics, 2, 1-45.

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.

Kato, J., &Mwakatobe, A. (2022). Fuel distribution inefficiencies and regional price disparities in Tanzania. Tanzania Journal of Development Studies, 14(2), 45-60.

Kilian, L. (2022). The economic effects of energy price shocks. Journal of Economic Literature, 60(1), 1-45. https://doi.org/10.1257/jel.20201314

Kiprop, S., & van der Merwe, L. (2024). Fuel price volatility and its impact on inflation in Kenya and South Africa. African Journal of Economic Studies, 12(3), 45-60.

Moulla, D. K., Attipoe, D., Mnkandla, E., & Abran, A. (2024). Predictive model of energy consumption using machine learning: a case study of residential buildings in South Africa. Sustainability, 16(11), 4365. https://doi.org/10.3390/su16114365 DOI: https://doi.org/10.3390/su16114365

Munyeka, W. (2023). Petrol price dynamics and economic resilience in Sub-Saharan Africa. Journal of Energy and Development, 48(1), 78-95.

Mwamunyange, L. (2023). The impact of rising fuel prices on household welfare in urban Tanzania. African Journal of Economic Research, 12(1), 78-95.

Nkengfack, H., &Fotio, H. K. (2021). Energy price shocks and household welfare in Sub-Saharan Africa: Evidence from panel data analysis. Energy Policy, 156, 112-123. https://doi.org/10.1016/j.enpol.2021.112123

Oluwaseun, A., & Adebayo, T. (2024). Trends in petrol prices and their economic implications in Sub-Saharan Africa. African Journal of Energy Economics, 9(1), 22-37.

Ramasubramanian, V. (2009). Time series analysis and forecasting: A practical approach. New Delhi, India: PHI Learning.

Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34(1), 248-255. https://doi.org/10.1016/j.eneco.2011.03.006 DOI: https://doi.org/10.1016/j.eneco.2011.03.006

Smith, A., & Johnson, B. (2024). Trends in global petrol prices: Causes and consequences. Energy Policy Review, 18(2), 45-60.

Downloads

Published

2025-09-19

How to Cite

Ilembo, B., & Nurdin, S. (2025). Short run forecasting of petroleum prices in Tanzania using ARIMA and exponential smoothing: A comparative analysis. African Scientific Annual Review, 2(1), 100-116. https://doi.org/10.51867/asarev.2.1.7

Share

Similar Articles

1-10 of 16

You may also start an advanced similarity search for this article.