Abstract
Crude oil prices are linked to significant economic activity in all nations across the world, since changes in crude oil prices usually impact the pricing of other commodities and services. As a result, forecasting crude oil prices has become a primary goal for academics and scientists alike. Crude oil has been the most important commodity in the world market and some countries like Nigeria, has it as the main trading commodity to other countries. Crude oil price fluctuations therefore cause problems on global economies and its effects are far reaching leading to either positive or negative economic growth rates. This study present an intelligent system that predicts the price of crude oil. The method used major economic factors that determine the price per barrel as inputs and outputs the price of crude oil. The data for usage came from the West Texas Intermediate (WTI) dataset, which spanned 24 years, and the experimental findings were quite hopeful, demonstrating that support vector machines could be used to forecast crude oil prices with a reasonable level of accuracy. Particle Swarm Optimization (PSO), Support Vector Machine (SVM), and K-Nearest Neighbors were employed in this investigation (KNN) for predicting Crude oil prices and the accuracy of the K-Nearest Neighbours was found to be higher than the Support Vector Machine by 9%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Naeem, M., El-Araby, H.M., Khalil, M.K., Jafri, M.K., Khan, F.: Integrated study of seismic and well data for porosity estimation using multi-attribute transforms: a case study of Boonsville Field, Fort Worth Basin, Texas, USA. Arab. J. Geosci. 8(10), 8777–8793 (2015). https://doi.org/10.1007/s12517-015-1806-7
Zhang, Y.J., Wei, Y.M.: The crude oil market and the gold market: evidence for cointegration, causality and price discovery. Resour. Policy 35(3), 168–177 (2010)
Gao, S., Lei, Y.: A new approach for crude oil price prediction based on stream learning. Geosci. Front. 8(1), 183–187 (2017)
Jiao, P., Alavi, A.H.: Artificial intelligence in seismology: advent, performance and future trends. Geosci. Front. 11(3), 739–744 (2020)
Cen, Z., Wang, J.: Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy 169, 160–171 (2019)
Esfahanipour, A., Aghamiri, W.: Adapted neuro-fuzzy inference system on indirect approach TSK fuzzy rule base for stock market analysis. Expert Syst. Appl. 37(7), 4742–4748 (2010)
Karasu, S., Altan, A., Bekiros, S., Ahmad, W.: A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy 212, 118750 (2020)
Wu, B., Wang, L., Lv, S.X., Zeng, Y.R.: Effective crude oil price forecasting using new text-based and big-data-driven model. Measurement 168, 108468 (2021)
McCollum, D.L., Jewell, J., Krey, V., Bazilian, M., Fay, M., Riahi, K.: Quantifying uncertainties influencing the long-term impacts of oil prices on energy markets and carbon emissions. Nat. Energy 1(7), 1–8 (2016)
Blazquez, D., Domenech, J.: Big Data sources and methods for social and economic analyses. Technol. Forecast. Soc. Chang. 130, 99–113 (2018)
Ghosh, M., Guha, R., Sarkar, R., Abraham, A.: A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput. Appl. 32(12), 7839–7857 (2019)
Awotunde, J.B., Ogundokun, R.O., Jimoh, R.G., Misra, S., Aro, T.O.: Machine learning algorithm for cryptocurrencies price prediction. Stud. Comput. Intell. 2021(972), 421–447 (2021)
Yinka-Banjo, C.O., Akinyemi, M.I., Nwadike, C.O., Misra, S., Oluranti, J., Damasevicius, R.: Unmanned vehicle model through markov decision process for pipeline inspection. In: Tripathi, M., Upadhyaya, S. (eds.) Conference Proceedings of ICDLAIR2019, pp. 317–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67187-7_33
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Adeniyi, E.A., Gbadamosi, B., Awotunde, J.B., Misra, S., Sharma, M.M., Oluranti, J. (2022). Crude Oil Price Prediction Using Particle Swarm Optimization and Classification Algorithms. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_128
Download citation
DOI: https://doi.org/10.1007/978-3-030-96308-8_128
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96307-1
Online ISBN: 978-3-030-96308-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)