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Linear Ensembles for WTI Oil Price Forecasting

Author

Listed:
  • João Lucas Ferreira dos Santos

    (Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology—Paraná, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Allefe Jardel Chagas Vaz

    (Graduate Program in Mechanical Engineering, Federal University of Technology—Paraná, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Yslene Rocha Kachba

    (Department of Industrial Engineering, Federal University of Technology—Paraná, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Sergio Luiz Stevan

    (Graduate Program in Electrical Engineering, Federal University of Technology—Paraná, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Thiago Antonini Alves

    (Graduate Program in Mechanical Engineering, Federal University of Technology—Paraná, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Hugo Valadares Siqueira

    (Department of Industrial Engineering, Federal University of Technology—Paraná, Ponta Grossa 84017-220, Brazil
    Graduate Program in Electrical Engineering, Federal University of Technology—Paraná, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

Abstract

This paper investigated the use of linear models to forecast crude oil futures prices (WTI) on a monthly basis, emphasizing their importance for financial markets and the global economy. The main objective was to develop predictive models using time series analysis techniques, such as autoregressive (AR), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), as well as ARMA variants adjusted by genetic algorithms (ARMA-GA) and particle swarm optimization (ARMA-PSO). Exponential smoothing techniques, including SES, Holt, and Holt-Winters , in additive and multiplicative forms, were also covered. The models were integrated using ensemble techniques, by the mean, median, Moore-Penrose pseudo-inverse, and weighted averages with GA and PSO. The methodology adopted included pre-processing that applied techniques to ensure the stationarity of the data, which is essential for reliable modeling. The results indicated that for one-step-ahead forecasts, the weighted average ensemble with PSO outperformed traditional models in terms of error metrics. For multi-step forecasts (3, 6, 9 and 12), the ensemble with the Moore-Penrose pseudo-inverse showed better results. This study has shown the effectiveness of combining predictive models to forecast future values in WTI oil prices, offering a useful tool for analysis and applications. However, it is possible to expand the idea of applying linear models to non-linear models.

Suggested Citation

  • João Lucas Ferreira dos Santos & Allefe Jardel Chagas Vaz & Yslene Rocha Kachba & Sergio Luiz Stevan & Thiago Antonini Alves & Hugo Valadares Siqueira, 2024. "Linear Ensembles for WTI Oil Price Forecasting," Energies, MDPI, vol. 17(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4058-:d:1457172
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    References listed on IDEAS

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    Cited by:

    1. Thomas Siqueira Pereira & Pedro Leineker Ochoski Machado & Barbara Dora Ross Veitia & Felipe Mercês Biglia & Paulo Henrique Dias dos Santos & Yara de Souza Tadano & Hugo Valadares Siqueira & Thiago An, 2024. "Application of Artificial Neural Networks in Predicting the Thermal Performance of Heat Pipes," Energies, MDPI, vol. 17(21), pages 1-25, October.

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