A lasso regression-based forecasting model for daily gasoline consumption: Türkiye Case

Year 2024, , 162 - 174, 19.01.2024

Ertuğrul Ayyıldız , Miraç Murat

https://doi.org/10.31127/tuje.1354501

Cited By: 3

Abstract

Gasoline is one of the most sought-after resources in the world, where the need for energy is indispensable and continuously increasing for human life today. A shortage of gasoline may negatively affect the economies of countries. Therefore, analysis and estimates about gasoline consumption are critical. Better forecast performance on gasoline consumption can serve the policymakers, managers, researchers, and other gasoline sector stakeholders. This study focuses on forecasting daily gasoline consumption in Türkiye using a lasso regression-based methodology. The methodology involves three main stages: cleaning data, extracting/selecting features, and forecasting future consumption. Additionally, Ridge Regression is employed for performance comparison. Results from the proposed methodology inform strategies for gasoline consumption, enabling more accurate planning and trade activities. The study emphasizes the importance of daily forecasts in deciding import quantities, facilitating timely planning, and establishing a well-organized gasoline supply chain system. Application of this methodology in Türkiye can pave the way for globally coordinated steps in gasoline consumption, establishing efficient gasoline supply chain systems. The findings provide insights for establishing a smooth and secure gasoline collection/distribution infrastructure, offering effective solutions to both public and private sectors. The proposed forecasting methodology serves as a reference for ensuring uninterrupted gasoline supply and maximizing engagement between customers and suppliers. Applied and validated for Türkiye, this methodology can guide global efforts, fostering planned approaches to gasoline consumption and enhancing supply chain systems.

Keywords

Gasoline consumption, Forecasting, Lasso regression, Ridge regression

Supporting Institution

Karadeniz Teknik Üniversitesi

Project Number

FBA-2023-10631

Thanks

This study was supported by Scientific Research Fund of the Karadeniz Technical University. Project Number: FBA-2023-10631.

References

• World Economic Outlook (2016). World Economic Outlook, April 2016: Too Slow for Too Long.
• The World Bank (2023). The World Bank in Türkiye. https://www.worldbank.org/en/country/turkey/overview
• T.C. Ticaret Bakanlığı (2023). Ekonomik Görünüm Mayıs 2023.
• Ertuğrul, N. A., Bağcı, Z. H., & Ertuğrul, Ö. L. (2018). Aquifer thermal energy storage systems: Basic concepts and general design methods. Turkish Journal of Engineering, 2(2), 38-48. https://doi.org/10.31127/tuje.340334
• Koyunoğlu, C. (2024). The economic case for blend fuels: A cost-benefit analysis in the European context. Sustainable Technology and Entrepreneurship, 3(2), 100060. https://doi.org/10.1016/j.stae.2023.100060
• Comert, M., & Yildiz, A. (2021). A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate. Turkish Journal of Engineering, 6(2), 178-189. https://doi.org/10.31127/tuje.903876
• Park, S. Y., & Yoo, S. H. (2014). The dynamics of oil consumption and economic growth in Malaysia. Energy Policy, 66, 218-223. https://doi.org/10.1016/j.enpol.2013.10.059
• Mikayilov, J. I., Mukhtarov, S., Dinçer, H., Yüksel, S., & Aydın, R. (2020). Elasticity analysis of fossil energy sources for sustainable economies: A case of gasoline consumption in Turkey. Energies, 13(3), 731. https://doi.org/10.3390/en13030731
• PETDER (2019). PETDER Sector Report.
• Aydın, Ü., Peker, H., & Gönülalan, A. U. (2020). Petrol Sektörü. Türkiye’nin Enerji Görünümü, 161-214
• Ma, H., & Zhang, Z. (2009). Grey prediction with Markov-Chain for Crude oil production and consumption in China. In The Sixth International Symposium on Neural Networks (ISNN 2009), 551-561. https://doi.org/10.1007/978-3-642-01216-7_58
• Wang, Q., & Song, X. (2019). Forecasting China's oil consumption: a comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM. Energy, 183, 160-171. https://doi.org/10.1016/j.energy.2019.06.139
• Yang, Z. Y., Chai, A. H., Yang, Y. F., Li, X. M., Li, P., & Dai, R. Y. (2016). The semiflexible polymer translocation into laterally unbounded region between two parallel flat membranes. Polymers, 8(9), 332. https://doi.org/10.3390/polym8090332
• Nel, W. P., & Cooper, C. J. (2008). A critical review of IEA's oil demand forecast for China. Energy Policy, 36(3), 1096-1106. https://doi.org/10.1016/j.enpol.2007.11.025
• Azadeh, A., Moghaddam, M., Khakzad, M., & Ebrahimipour, V. (2012). A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting. Computers & Industrial Engineering, 62(2), 421-430. https://doi.org/10.1016/j.cie.2011.06.019
• Narayan, P. K., & Wong, P. (2009). A panel data analysis of the determinants of oil consumption: the case of Australia. Applied Energy, 86(12), 2771-2775. https://doi.org/10.1016/j.apenergy.2009.04.035
• Yang, Y., Chen, Y., Shi, J., Liu, M., Li, C., & Li, L. (2016). An improved grey neural network forecasting method based on genetic algorithm for oil consumption of China. Journal of Renewable and Sustainable Energy, 8(2), 024104. https://doi.org/10.1063/1.4944977
• Li, J., Wang, R., Wang, J., & Li, Y. (2018). Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms. Energy, 144, 243-264. https://doi.org/10.1016/j.energy.2017.12.042
• Assareh, E., Behrang, M. A., Assari, M. R., & Ghanbarzadeh, A. (2010). Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy, 35(12), 5223-5229. https://doi.org/10.1016/j.energy.2010.07.043
• Lin, B., & Xie, C. (2013). Estimation on oil demand and oil saving potential of China's road transport sector. Energy Policy, 61, 472-482. https://doi.org/10.1016/j.enpol.2013.06.017
• Rao, R. D., & Parikh, J. K. (1996). Forecast and analysis of demand for petroleum products in India. Energy Policy, 24(6), 583-592. https://doi.org/10.1016/0301-4215(96)00019-5
• Wang, Q., & Song, X. (2019). Forecasting China's oil consumption: a comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM. Energy, 183, 160-171. https://doi.org/10.1016/j.energy.2019.06.139
• Duan, H., Lei, G. R., & Shao, K. (2018). Forecasting crude oil consumption in China using a grey prediction model with an optimal fractional-order accumulating operator. Complexity, 3869619 https://doi.org/10.1155/2018/3869619
• Behrang, M. A., Assareh, E., Ghalambaz, M., Assari, M. R., & Noghrehabadi, A. R. (2011). Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm). Energy, 36(9), 5649-5654. https://doi.org/10.1016/j.energy.2011.07.002
• Minniear, M. P. (2000). Forecasting the permanent decline in global petroleum production. Journal of Geoscience Education, 48(2), 130-136. https://doi.org/10.5408/1089-9995-48.2.130
• Al-Qaness, M. A., Abd Elaziz, M., & Ewees, A. A. (2018). Oil consumption forecasting using optimized adaptive neuro-fuzzy inference system based on sine cosine algorithm. IEEE Access, 6, 68394-68402. https://doi.org/10.1109/ACCESS.2018.2879965
• Fatima, T., Xia, E., & Ahad, M. (2019). Oil demand forecasting for China: a fresh evidence from structural time series analysis. Environment, Development and Sustainability, 21, 1205-1224. https://doi.org/10.1007/s10668-018-0081-7
• Yu, L., Zhao, Y., Tang, L., & Yang, Z. (2019). Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting, 35(1), 213-223. https://doi.org/10.1016/j.ijforecast.2017.11.005
• Keshavarzian, M., Anaraki, S. K., Zamani, M., & Erfanifard, A. (2012). Projections of oil demand in road transportation sector on the basis of vehicle ownership projections, worldwide: 1972–2020. Economic Modelling, 29(5), 1979-1985. https://doi.org/10.1016/j.econmod.2012.06.009
• Sadri, A., Ardehali, M. M., & Amirnekooei, K. (2014). General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN. Energy, 77, 831-843. https://doi.org/10.1016/j.energy.2014.09.067
• Melikoglu, M. (2014). Demand forecast for road transportation fuels including gasoline, diesel, LPG, bioethanol and biodiesel for Turkey between 2013 and 2023. Renewable Energy, 64, 164-171. https://doi.org/10.1016/j.renene.2013.11.009
• Azadeh, A., Behmanesh, I., Vafa Arani, H., & Sadeghi, M. H. (2014). An integrated fuzzy mathematical programming-analysis of variance approach for forecasting gasoline consumption with ambiguous inputs: USA, Canada, Japan, Iran and Kuwait. International Journal of Industrial and Systems Engineering, 18(2), 159-184. https://doi.org/10.1504/IJISE.2014.064704
• Sapnken, E. F., Tamba, J. G., Essiane, S. N., Koffi, F. D., & Njomo, D. (2018). Modeling and forecasting gasoline consumption in Cameroon using linear regression models. International Journal of Energy Economics and Policy, 8(2), 111-120.
• Anggarani, R., & Watada, J. (2012). A gasoline consumption model based on the harmony search algorithm: Study case of Indonesia. Intelligent Decision Technologies, 6(3), 233-241. https://doi.org/10.3233/IDT-2012-0139
• Chen, H., Tong, Y., & Wu, L. (2021). Forecast of energy consumption based on FGM (1, 1) model. Mathematical Problems in Engineering, 2021, 1-11. https://doi.org/10.1155/2021/6617200
• Güngör, B. O., Ertuğrul, H. M., & Soytaş, U. (2021). Impact of Covid-19 outbreak on Turkish gasoline consumption. Technological Forecasting and Social Change, 166, 120637. https://doi.org/10.1016/j.techfore.2021.120637
• Wang, Q., Li, S., & Li, R. (2018). Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques. Energy, 161, 821-831. https://doi.org/10.1016/j.energy.2018.07.168
• Wang, Q., Li, S., Li, R., & Ma, M. (2018). Forecasting US shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model. Energy, 160, 378-387. https://doi.org/10.1016/j.energy.2018.07.047
• Wang, Q., Li, S., Zhang, M., & Li, R. (2022). Impact of COVID-19 pandemic on oil consumption in the United States: a new estimation approach. Energy, 239, 122280. https://doi.org/10.1016/j.energy.2021.122280
• Wang, Q., Li, S., & Jiang, F. (2021). Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China. Journal of Cleaner Production, 313, 127897. https://doi.org/10.1016/j.jclepro.2021.127897
• Wang, Q., Li, S., Li, R., & Jiang, F. (2022). Underestimated impact of the COVID-19 on carbon emission reduction in developing countries–a novel assessment based on scenario analysis. Environmental Research, 204, 111990. https://doi.org/10.1016/j.envres.2021.111990
• Ogutu, J. O., Schulz-Streeck, T., & Piepho, H. P. (2012). Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC proceedings, 6, 1-6. https://doi.org/10.1186/1753-6561-6-S2-S10
• Farvahari, A., Gozashti, M. H., & Dehesh, T. (2019). The usage of lasso, ridge, and linear regression to explore the most influential metabolic variables that affect fasting blood sugar in type 2 Diabetes patients. Romanian Journal of Diabetes Nutrition and Metabolic Diseases, 26(4), 371-379. https://doi.org/10.2478/rjdnmd-2019-0040
• Altelbany, S. (2021). Evaluation of ridge, elastic net and lasso regression methods in precedence of multicollinearity problem: A simulation study. Journal of Applied Economics and Business Studies, 5(1), 131-142. https://doi.org/10.34260/jaebs.517
• McNeish, D. M. (2015). Using lasso for predictor selection and to assuage overfitting: A method long overlooked in behavioral sciences. Multivariate Behavioral Research, 50(5), 471-484. https://doi.org/10.1080/00273171.2015.1036965
• Trivedi, U. B., Bhatt, M., & Srivastava, P. (2021). Prevent overfitting problem in machine learning: a case focus on linear regression and logistics regression. Innovations in Information and Communication Technologies (IICT-2020) Proceedings of International Conference on ICRIHE-2020, Delhi, India: IICT-2020, 345-349. https://doi.org/10.1007/978-3-030-66218-9_40
• Jovanovic, M., Radovanovic, S., Vukicevic, M., Van Poucke, S., & Delibasic, B. (2016). Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression. Artificial İntelligence in Medicine, 72, 12-21. https://doi.org/10.1016/j.artmed.2016.07.003
• Jacobson, N. C., Chow, S. M., & Newman, M. G. (2019). The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data. Behavior Research Methods, 51, 295-315. https://doi.org/10.3758/s13428-018-1101-0
• Chapman, C. S., Gallivan, J. P., Wood, D. K., Milne, J. L., Culham, J. C., & Goodale, M. A. (2010). Reaching for the unknown: multiple target encoding and real-time decision-making in a rapid reach task. Cognition, 116(2), 168-176. https://doi.org/10.1016/j.cognition.2010.04.008
• T.C. Enerji ve Tabi Kaynaklar Bakanlığı (2020). EIGM Raporları
• Keogh, E., Chu, S., Hart, D., & Pazzani, M. (2004). Segmenting time series: A survey and novel approach. In Data mining in time series databases, 1-21. https://doi.org/10.1142/9789812565402_0001
• Pereira, C. V. (2021). Portfolio efficiency with high-dimensional data as conditioning information. International Review of Financial Analysis, 77, 101811. https://doi.org/10.1016/j.irfa.2021.101811
• Çınaroğlu, S. (2017). Sağlık harcamasının tahmininde makine öğrenmesi regresyon yöntemlerinin karşılaştırılması. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 22(2), 179-200. https://doi.org/10.17482/uumfd.338805
• Teixeira, A. L., Leal, J. P., & Falcao, A. O. (2013). Random forests for feature selection in QSPR Models-an application for predicting standard enthalpy of formation of hydrocarbons. Journal of Cheminformatics, 5, 1-15. https://doi.org/10.1186/1758-2946-5-9
• Omranian, N., Eloundou-Mbebi, J. M., Mueller-Roeber, B., & Nikoloski, Z. (2016). Gene regulatory network inference using fused LASSO on multiple data sets. Scientific reports, 6(1), 20533. https://doi.org/10.1038/srep20533
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
• Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: the lasso and generalizations. CRC Press.
• Wang, F., Liu, X., Liu, C., Li, H., & Han, Q. (2018). Remaining useful life prediction method of rolling bearings based on Pchip-EEMD-GM (1, 1) model. Shock and vibration, 3013684. https://doi.org/10.1155/2018/3013684
• Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of Econometrics, 54(1-3), 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
• Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.