A novel ensemble method for the accurate prediction of the major oil prices in Tanzania

GODFREY JOSEPH SAQWARE, Ismail B

Abstract


Global development relies much on oil to run different types of machines. Using oil to power many types of equipment is very important to world economic growth. The analysis of oil prices is crucial for the country's long-term stability. However, global monopoly producers, wars, and pandemics have contributed to the volatility of crude oil prices. As a result, the optimal prediction model for oil prices becomes crucial. The performance of several ensemble strategies on single traditional and machine learning models was examined in this study. We found that the weighted ensemble technique outperformed other ensemble and single models in predicting petrol and diesel prices in Tanzania based on four performance metrics. Furthermore, a spike in global oil prices necessitates global economic and political stability for non-oil-producing nations to avoid suffering the consequences. Finally, other ensemble approaches may be used and compared to predict the oil prices. 

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References


A. Demirbas, B. Omar Al-Sasi, and A.-S. Nizami, “Recent volatility in the price of crude oil,” Energy Sources, Part B: Economics, Planning, and Policy, vol. 12, no. 5, pp. 408–414, 2017.

D. Zhang, Q. Ji, and A. M. Kutan, “Dynamic transmission mechanisms in global crude oil prices: estimation and implications,” Energy, vol. 175, pp. 1181–1193, 2019.

M. Zavadska, L. Morales, and J. Coughlan, “Brent crude oil prices volatility during major crises,” Finance Research Letters, vol. 32, p. 101078, 2020.

Y. He, S. Wang, and K. K. Lai, “Global economic activity and crude oil prices: A cointegration analysis,” Energy Economics, vol. 32, no. 4, pp. 868–876, 2010.

R. A. Ratti and J. L. Vespignani, “Why are crude oil prices high when global activity is weak?,” Economics Letters, vol. 121, no. 1, pp. 133–136, 2013.

M. Dong, C.-P. Chang, Q. Gong, and Y. Chu, “Revisiting global economic activity and crude oil prices: A wavelet analysis,” Economic Modelling, vol. 78, pp. 134–149, 2019.

F. Picciolo, A. Papandreou, K. Hubacek, and F. Ruzzenenti, “How crude oil prices shape the global division of labor,” Applied Energy, vol. 189, pp. 753–761, 2017.

L. H. Ederington, C. S. Fernando, T. K. Lee, S. C. Linn, and H. Zhang, “The relation between petroleum product prices and crude oil prices,” Energy Economics, vol. 94, p. 105079, 2021.

A. R. Kira, “The factors affecting Gross Domestic Product (GDP) in developing countries: The case of Tanzania,” 2013.

B. B. Stambuli, “Price and income elasticities of oil demand in Tanzania: An autoregressive approach,” Business Management Dynamics, vol. 3, no. 1, p. 75, 2013.

V. Pershin, J. C. Molero, and F. P. de Gracia, “Exploring the oil prices and exchange rates nexus in some African economies,” Journal of Policy Modeling, vol. 38, no. 1, pp. 166–180, 2016.

W. Anderson, “Local suppliers in Tanzania: Ready for the petroleum sector?,” The African Review: A Journal of African Politics, Development and International Affairs, pp. 51–83, 2016.

R. H. Pedersen and P. Bofin, The politics of gas contract negotiations in Tanzania: a review. JSTOR, 2015.

A. Kinyondo and E. Villanger, “Local content requirements in the petroleum sector in Tanzania: A thorny road from inception to implementation?,” The Extractive Industries and Society, vol. 4, no. 2, pp. 371–384, 2017.

H. Allende and C. Valle, “Ensemble methods for time series forecasting,” in Claudio moraga: A passion for multi-valued logic and soft computing, Springer, 2017, pp. 217–232.

N. Kourentzes, D. K. Barrow, and S. F. Crone, “Neural network ensemble operators for time series forecasting,” Expert Systems with Applications, vol. 41, no. 9, pp. 4235–4244, 2014.

M. Oliveira and L. Torgo, “Ensembles for time series forecasting,” in Asian Conference on Machine Learning, 2015, pp. 360–370.

R. Adhikari, “A neural network based linear ensemble framework for time series forecasting,” Neurocomputing, vol. 157, pp. 231–242, 2015.

G. Song and Q. Dai, “A novel double deep ELMs ensemble system for time series forecasting,” Knowledge-Based Systems, vol. 134, pp. 31–49, 2017.

J. Y. Choi and B. Lee, “Combining LSTM network ensemble via adaptive weighting for improved time series forecasting,” Mathematical Problems in Engineering, vol. 2018, 2018.

M. M. Rahman, M. M. Islam, K. Murase, and X. Yao, “Layered ensemble architecture for time series forecasting,” IEEE transactions on cybernetics, vol. 46, no. 1, pp. 270–283, 2015.

A. Galicia, R. Talavera-Llames, A. Troncoso, I. Koprinska, and F. Martínez-Álvarez, “Multi-step forecasting for big data time series based on ensemble learning,” Knowledge-Based Systems, vol. 163, pp. 830–841, 2019.

S. Zhang, Y. Chen, W. Zhang, and R. Feng, “A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting,” Information Sciences, vol. 544, pp. 427–445, 2021.

L. Du, R. Gao, P. N. Suganthan, and D. Z. Wang, “Bayesian optimization based dynamic ensemble for time series forecasting,” Information Sciences, vol. 591, pp. 155–175, 2022.

S. J. Taylor and B. Letham, “Forecasting at scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, 2018.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794.

B. M. Dillon and C. B. Barrett, “Global oil prices and local food prices: Evidence from East Africa,” American Journal of Agricultural Economics, vol. 98, no. 1, pp. 154–171, 2016.

S. Cheng and Y. Cao, “On the relation between global food and crude oil prices: an empirical investigation in a nonlinear framework,” Energy Economics, vol. 81, pp. 422–432, 2019.

L. Yu, W. Dai, and L. Tang, “A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting,” Engineering Applications of Artificial Intelligence, vol. 47, pp. 110–121, 2016.

Y. Ding, “A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting,” Energy, vol. 154, pp. 328–336, 2018.

L. A. Gabralla, H. Mahersia, and A. Abraham, “Ensemble neurocomputing based oil price prediction,” in Afro-European Conference for Industrial Advancement, 2015, pp. 293–302.

Y. Zhou, T. Li, J. Shi, and Z. Qian, “A CEEMDAN and XGBOOST-based approach to forecast crude oil prices,” Complexity, vol. 2019, 2019.


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