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A Comparative Study of Time Series, Machine Learning, and Ensemble Models for Crude Oil Price Prediction

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 914)


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

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  • DOI: 10.1007/978-981-19-2980-9_13
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Correspondence to Sunil Kumar Singh .

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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.

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