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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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    1. Mansi Ghodsi & Hossein Hassani & Donya Rahmani & Emmanuel Sirimal Silva, 2018. "Vector and recurrent singular spectrum analysis: which is better at forecasting?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(10), pages 1872-1899, July.
    2. 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.
    3. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    4. 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.
    5. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    6. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    7. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    8. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
    9. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
    10. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    11. Kolidakis, Stylianos & Botzoris, George & Profillidis, Vassilios & Lemonakis, Panagiotis, 2019. "Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis," Economic Analysis and Policy, Elsevier, vol. 64(C), pages 159-171.
    12. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
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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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