IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i16p4163-d1460763.html
   My bibliography  Save this article

The Impact Time Series Selected Characteristics on the Fuel Demand Forecasting Effectiveness Based on Autoregressive Models and Markov Chains

Author

Listed:
  • Paweł Więcek

    (Faculty of Civil Engineering, Cracow University of Technology, 31-155 Krakow, Poland)

  • Daniel Kubek

    (Faculty of Civil Engineering, Cracow University of Technology, 31-155 Krakow, Poland)

Abstract

This article examines the influence of specific time series attributes on the efficacy of fuel demand forecasting. By utilising autoregressive models and Markov chains, the research aims to determine the impact of these attributes on the effectiveness of specific models. The study also proposes modifications to these models to enhance their performance in the context of the fuel industry’s unique fuel distribution. The research involves a comprehensive analysis, including identifying the impact of volatility, seasonality, trends, and sudden shocks within time series data on the suitability and accuracy of forecasting methods. The paper utilises ARIMA, SARIMA, and Markov chain models to assess their ability to integrate diverse time series features, improve forecast precision, and facilitate strategic logistical planning. The findings suggest that recognising and leveraging these time series characteristics can significantly enhance the management of fuel supplies, leading to reduced operational costs and environmental impacts.

Suggested Citation

  • Paweł Więcek & Daniel Kubek, 2024. "The Impact Time Series Selected Characteristics on the Fuel Demand Forecasting Effectiveness Based on Autoregressive Models and Markov Chains," Energies, MDPI, vol. 17(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4163-:d:1460763
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/16/4163/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/16/4163/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sushil Punia & Konstantinos Nikolopoulos & Surya Prakash Singh & Jitendra K. Madaan & Konstantia Litsiou, 2020. "Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4964-4979, July.
    2. Yajun Zhou & Lilei Wang & Rong Zhong & Yulong Tan, 2018. "A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, January.
    3. Tsiliyannis, Christos Aristeides, 2018. "Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age," European Journal of Operational Research, Elsevier, vol. 271(2), pages 474-489.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Abraham & Wang, Jason X. & Farooque, Muhammad & Wang, Yulan & Choi, Tsan-Ming, 2021. "Multi-dimensional circular supply chain management: A comparative review of the state-of-the-art practices and research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    2. Gunasekara, Lahiru & Robb, David J. & Zhang, Abraham, 2023. "Used product acquisition, sorting and disposition for circular supply chains: Literature review and research directions," International Journal of Production Economics, Elsevier, vol. 260(C).
    3. Naragain Phumchusri & Nichakan Phupaichitkun, 2024. "Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 461-480, October.
    4. Keun Hee Lee & Mali Abdollahian & Sergei Schreider & Sona Taheri, 2023. "Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
    5. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).
    6. Cui, Hailong & Rajagopalan, Sampath & Ward, Amy R., 2020. "Predicting product return volume using machine learning methods," European Journal of Operational Research, Elsevier, vol. 281(3), pages 612-627.
    7. Carlos Cuartas & Jose Aguilar, 2023. "Hybrid algorithm based on reinforcement learning for smart inventory management," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 123-149, January.
    8. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    9. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    10. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.
    11. Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
    12. Thais de Castro Moraes & Xue‐Ming Yuan & Ek Peng Chew, 2024. "Hybrid convolutional long short‐term memory models for sales forecasting in retail," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1278-1293, August.
    13. Sujae Kim & Sangho Choo & Gyeongjae Lee & Sanghun Kim, 2022. "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method," Sustainability, MDPI, vol. 14(5), pages 1-15, February.
    14. Yousif Alyousifi & Kamarulzaman Ibrahim & Mahmod Othamn & Wan Zawiah Wan Zin & Nicolas Vergne & Abdullah Al-Yaari, 2022. "Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
    15. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
    16. Truong Ngoc Cuong & Le Ngoc Bao Long & Hwan-Seong Kim & Sam-Sang You, 2023. "Data analytics and throughput forecasting in port management systems against disruptions: a case study of Busan Port," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 61-89, March.
    17. Zhou, Yu & Chen, Yang & Liu, Shenyan & Kou, Gang, 2024. "Availability simulation and transfer prediction for bike sharing systems based on discrete event simulation," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    18. De Lima, Felipe Alexandre & Seuring, Stefan, 2023. "A Delphi study examining risk and uncertainty management in circular supply chains," International Journal of Production Economics, Elsevier, vol. 258(C).
    19. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    20. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4163-:d:1460763. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.