黑料吃瓜网

Home⇛Isabela State University Linker: Journal of Engineering, Computing and Technology⇛vol. 1 no. 1 (2024)

Forecasting Gasoline Retail Prices Using Predictive Analytics

Joseph T. Banatao | Vince Aldrin F. Panganiban | Igancio F. Datul | Amy Lyn M. Maddalora

Discipline: Artificial Intelligence

 

Abstract:

Forecasting gasoline and diesel retail prices is crucial for consumers, businesses, and policymakers due to the significant economic impact of fuel costs. This study aimed to address the challenge of predicting these prices using predictive analytics. It sought to identify weekly price trends, develop accurate forecasting models for various types of gasoline and diesel, and create an application to integrate these models. Data from Kaggle, encompassing 1,362 instances with attributes (date, regular, midgrade, premium, and diesel) over 26 years (1995-2021), were analyzed. The findings indicated inconsistent prices across gasoline types and a recent sharp decline in retail prices. Using the ARIMA (1,0,0) model, the study developed a forecasting model with minimal differences between actual and forecasted prices. The five-week forecast revealed a steady price increase, with a low RMSE of 0.22 to 0.26, indicating excellent model accuracy. Additionally, an application with a dashboard for visualizing weekly gasoline and diesel price trends was developed. This study successfully provides a model for forecasting retail gasoline prices, helping consumers anticipate price increases.



References:

  1. Anderson, S. T., Kellogg, R., Sallee, J. M., & Curtin, R. T. (2011). Forecasting gasoline prices using consumer surveys. American Economic Review, 101(3), 110–114.
  2. Bounteous. (2020, September 15). Forecasting with a time series model using Python: Part one. Senior Data Scientist.
  3. Bora, N. (2021). Understanding ARIMA model for machine learning. Capital One Tech.
  4. Chandra, et al. (2020). Predictive analytics in big data. Journal of Information and Computation Science, 43. .
  5. Edwards, J. (2023). What is predictive analytics? Transforming data into future insights. CIO.
  6. Georgiou, A. D. (2021). An introduction to time series forecasting. InfoWorld – New Tech Forum.
  7. He, X. J. (2023). Forecasting gasoline price with time series models. Communications of the IIMA, 21(1), Article 1. https://doi.org/10.58729/1941-6687.1440
  8. Iyke, B. N. (2019). Real output and oil price uncertainty in an oil-producing country. Bulletin of Monetary Economics and Banking, 22(2), 163–176.
  9. Lam, W. D. (2014). A survey of predictive analytics in data mining with big data (Master’s thesis). Athabasca University.
  10. LaRiviere, J., McAfee, P., Rao, J., Narayanan, V. K., & Sun, W. (2016, May 25). Where predictive analytics is having the biggest impact. Harvard Business Review.
  11. Miguel, C. G. (2021, January 27). What is the difference between regular and premium fuel? Philkotse.com.
  12. Nyangarika, A., Mikhaylov, A., & Richter, U. H. (2018). Oil price factors: Forecasting on the base of modified ARIMA model. International Journal of Energy Economics and Policy, 9(1), 149–159. https://doi.org/10.32479/ijeep.6812
  13. Octane Fuel. (2018, January 2). Shamrock Tire & Auto Repair News-Center. Retrieved from
  14. O’Hara, R., Haylon, L., & Boyle, D. (2023). A data analytics mindset with CRISP-DM. SF Magazine. Retrieved from
  15. Pierre, S. (2021). A guide for time series forecasting in Python. WorldQuant Predictive & Built In. Retrieved from
  16. Sabu, K. M., & Kumar, T. K. M. (2020). Predictive analytics in agriculture: Forecasting prices of arecanuts in Kerala. Procedia Computer Science, 171, 699–708.
  17. Shah, G. N. (2015, April 14). 5 reasons VB.NET is better than C#. MigrateTo.net.
  18. Simplilearn. (2021). Understanding time series analysis in Python. Simplilearn Tutorials. Retrieved from
  19. Su, M., Zhang, Z., Zhu, Y., Zha, D., & Wen, W. (2019). Data-driven natural gas spot price prediction models using machine learning methods. Energies, 12(9), Article 1680 (or pp. 1-17).
  20. Sun, L., et al. (2018). Demand forecasting for petrol products in gas stations using clustering and decision tree. Advance Computational Intelligence and Intelligent Informatics, 22(3), 387–393.
  21. Tyler, C. (2022, April 16). What is VB.NET? Introduction & features. GURU99.
  22. U.S. Energy Information Administration. (2021, January). U.S. gasoline and diesel retail prices 1995-2021 [Data set]. Kaggle.
  23. Uddin, S. M., Rahman, A., & Ansari, E. U. (2018). Comparison of some statistical forecasting techniques with GMDH predictor: A case study. Journal of Mechanical Engineering, 47(1), 16–21.
  24. U.S. Energy Information Administration (January 2021). U.S. gasoline and diesel retail prices 1995-2021. .
  25. Vandeput, N. (2019). Forecast KPIs: RMSE, MAE, MAPE and bias. Data Science for Supply Chain Forecasting.
  26. Varshney, P. (2020). Measure performance for a time series model (ETS or ARIMA). Towards Data Science.
  27. Vijay, A. M., Devi, T., Poornima, N., & Gnanavel, R. (2021). Accuracy acquirement for petrol prices prediction using machine learning enhanced random forest algorithm. Turkish Online Journal of Qualitative Inquiry, 12(3), 2468-2474.  
  28. Vijiyan, S. (2022). Time series analysis of average fuel prices (2000-2021).

Tools


Copyright © 2025   黑料吃瓜网