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Fusing Nature with Computational Science for Optimal Signal Extraction

Author

Listed:
  • Hossein Hassani

    (Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran
    These authors contributed equally to this work.)

  • Mohammad Reza Yeganegi

    (Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran
    These authors contributed equally to this work.)

  • Xu Huang

    (Leicester Castle Business School, De Montfort University, Leicester LE1 9BH, UK
    These authors contributed equally to this work.)

Abstract

Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.

Suggested Citation

  • Hossein Hassani & Mohammad Reza Yeganegi & Xu Huang, 2021. "Fusing Nature with Computational Science for Optimal Signal Extraction," Stats, MDPI, vol. 4(1), pages 1-15, January.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:1:p:6-85:d:482984
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    References listed on IDEAS

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    10. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
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    Cited by:

    1. Mohammad Reza Yeganegi & Hossein Hassani & Rangan Gupta, 2023. "The ENSO cycle and forecastability of global inflation and output growth: Evidence from standard and mixed‐frequency multivariate singular spectrum analyses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1690-1707, November.

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