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Fusing Nature with Computational Science for Optimal Signal Extraction

by 1,*,†, 2,† and 3,†
1
Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran
2
Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran
3
Leicester Castle Business School, De Montfort University, Leicester LE1 9BH, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 31 October 2020 / Revised: 30 December 2020 / Accepted: 12 January 2021 / Published: 19 January 2021
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. View Full-Text
Keywords: forecasting; Singular Spectrum Analysis; genetic algorithm forecasting; Singular Spectrum Analysis; genetic algorithm
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MDPI and ACS Style

Hassani, H.; Yeganegi, M.R.; Huang, X. Fusing Nature with Computational Science for Optimal Signal Extraction. Stats 2021, 4, 71-85. https://0-doi-org.brum.beds.ac.uk/10.3390/stats4010006

AMA Style

Hassani H, Yeganegi MR, Huang X. Fusing Nature with Computational Science for Optimal Signal Extraction. Stats. 2021; 4(1):71-85. https://0-doi-org.brum.beds.ac.uk/10.3390/stats4010006

Chicago/Turabian Style

Hassani, Hossein, Mohammad R. Yeganegi, and Xu Huang. 2021. "Fusing Nature with Computational Science for Optimal Signal Extraction" Stats 4, no. 1: 71-85. https://0-doi-org.brum.beds.ac.uk/10.3390/stats4010006

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