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Some variation of COBRA in sequential learning setup

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  • Aryan Bhambu
  • Arabin Kumar Dey

Abstract

This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behaviour of prediction. We compare the performance of the model based on two types of hyper-parameter tuning Bayesian optimisation (BO) and Usual Grid search. Our proposed methodologies outperform all state-of-the-art comparative models. We illustrate the methodologies through eight time series datasets from three categories: cryptocurrency, stock index, and short-term load forecasting.

Suggested Citation

  • Aryan Bhambu & Arabin Kumar Dey, 2024. "Some variation of COBRA in sequential learning setup," Papers 2405.04539, arXiv.org.
    • Handle: RePEc:arx:papers:2405.04539
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    References listed on IDEAS

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    1. Yang, Yuhong, 2000. "Combining Different Procedures for Adaptive Regression," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 135-161, July.
    2. Abbasimehr, Hossein & Paki, Reza, 2021. "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Biau, Gérard & Fischer, Aurélie & Guedj, Benjamin & Malley, James D., 2016. "COBRA: A combined regression strategy," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 18-28.
    4. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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