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Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model

Author

Listed:
  • A. Usha Ruby

    (VIT Bhopal University)

  • J. George Chellin Chandran

    (VIT Bhopal University)

  • B. N. Chaithanya

    (GITAM School of Technology)

  • T. J. Swasthika Jain

    (GITAM School of Technology)

  • Renuka Patil

    (GITAM School of Technology)

Abstract

There is an urgent need for the prediction of oil price; in addition, for various small and large industries, individuals, and the government, it is a blessing. Nevertheless, owing to the nonlinear inherent in crude oil price (COP), market inter-relationship in oil price time series, chaotic behavior of the COP, and inherent fractality in oil price, the prediction of COP is tedious. By employing a polyharmony spline centered recurrent neural network (PS-RNN) the work presented a new framework of COP prediction to tackle those problems. By deploying the cubic hermite spline grounded on empirical mode decomposition, which offers decomposed data, the system tackles the intrinsic characteristics of fluctuating data. The knowledgeable features are extracted using simple moving average (SMA), and exponential moving average technical indicators. By utilizing the Gaussian distribution-centric Aquila optimization that aids to endure nonlinear inherent of data within low computation time, the most related features are elected. Finally, the chosen features get trained to PS-RNN. Regarding mean absolute error, root mean squared error , mean absolute percent error (MAPE), along with Symmetric MAPE, the system obtains a low relative fault along with prevents misprediction of data; in addition, sustains higher to prevailing techniques.

Suggested Citation

  • A. Usha Ruby & J. George Chellin Chandran & B. N. Chaithanya & T. J. Swasthika Jain & Renuka Patil, 2024. "Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1295-1314, August.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10460-w
    DOI: 10.1007/s10614-023-10460-w
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    References listed on IDEAS

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    1. Wang, Jue & Zhou, Hao & Hong, Tao & Li, Xiang & Wang, Shouyang, 2020. "A multi-granularity heterogeneous combination approach to crude oil price forecasting," Energy Economics, Elsevier, vol. 91(C).
    2. Chen, Yanhui & Zhang, Chuan & He, Kaijian & Zheng, Aibing, 2018. "Multi-step-ahead crude oil price forecasting using a hybrid grey wave model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 98-110.
    3. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    4. Yang, Lu, 2019. "Connectedness of economic policy uncertainty and oil price shocks in a time domain perspective," Energy Economics, Elsevier, vol. 80(C), pages 219-233.
    5. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    6. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    7. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    8. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    9. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    10. Huang, Lili & Wang, Jun, 2018. "Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network," Energy, Elsevier, vol. 151(C), pages 875-888.
    11. Hualing Lin & Qiubi Sun, 2020. "Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks," Energies, MDPI, vol. 13(7), pages 1-21, March.
    12. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
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