Fuel costs are falling worldwide during the pandemic, but not in India. In recent years, the petrol’s price has risen dramatically in all across the states of India, and reaching in three digits. The government is also targeting it as their best revenue source. It indicates that the fuel costs causes more inflation and common people have to suffer. These increased prices have robbed Indian citizens of living a healthy life. This paper analyzes Delhi’s petrol price and forecasting by examining daily petrol prices’ forecasting performance from June 16, 2017, to December 15, 2021. Various time series, machine learning, deep learning, and ensemble learning models are used to find the best model for forecasting the results. We have analyzed the performance of the models with the help of the performance metrics such as MSE, RMSE, MAPE, and NRMSE. The outcomes of the models indicate that time series-based models are pretty compelling.
- Machine learning
- Deep learning
- Statistical models
- Crude oil
- Time series
This is a preview of subscription content, access via your institution.
Gabhane, D., Gabhane, M.: Rising prices of petrol and diesel in India since 2014: an analytical study. PalArch’s J. Archaeol. Egypt/Egyptology 18(7), 2309–2315 (2021)
Bhattacharya, B., Batra, A.: Fuel pricing policy reform in India: implications and way forward. Econ. Polit. Wkly. 77–86 (2009)
Shambulingappa, H.: Crude oil price forecasting using machine learning. Int. J. Adv. Sci. Innov. 1(1) (2020)
Yao, T., Zhang, Y.-J.: Forecasting crude oil prices with the Google index. Energy Procedia 105, 3772–3776 (2017)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Inf. Sci. 174(3–4), 219–235 (2005)
Kumar, S., Mishra, S., Singh, S.K.: Deep transfer learning-based COVID-19 prediction using chest X-rays. J. Health Manag. 23(04), 730–746 (2021). https://doi.org/10.1177/09720634211050425
Chen, Y., He, K., Tso, G.K.: Forecasting crude oil prices: a deep learning based model. Procedia Comput. Sci. 122, 300–307 (2017)
Sagheer, A., Kotb, M.: Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323, 203–213 (2019)
Garg, C. et al.: Adaptive fuzzy logic models for the prediction of compressive strength of sustainable concrete. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol. 218. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_47
Chakraborty, R. et al.: Study and prediction analysis of the employee turnover using machine learning approaches. In: 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), pp. 1–6 (2021). https://doi.org/10.1109/GUCON50781.2021.9573759.
Claesen, M., De Smet, F., Suykens, J.A., De Moor, B.: Fast prediction with SVM models containing RBF kernels. arXiv preprint arXiv:1403.0736 (2014)
Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181–1191 (2020)
Kumar, S., Mishra, S., Singh, S.K.: A machine learning-based model to estimate PM2. 5 concentration levels in Delhi’s atmosphere. Heliyon 6(11), e05618 (2020)
Ju, Y., Sun, G., Chen, Q., Zhang, M., Zhu, H., Rehman, M.U.: A model combining convolutional neural network and lightgbm algorithm for ultra-short-term wind power forecasting. IEEE Access 7, 28309–28318 (2019)
Kumari, S., Singh, S.K.: Machine learning-based time series models for effective CO2 emission prediction in India (2022). Available on: https://doi.org/10.21203/rs.3.rs-1265771/v1
Editors and Affiliations
Rights and permissions
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prakash, A., Singh, S.K. (2022). A Comparative Study of Time Series, Machine Learning, and Ensemble Models for Crude Oil Price Prediction. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_13
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2979-3
Online ISBN: 978-981-19-2980-9
eBook Packages: Computer ScienceComputer Science (R0)