Fusing Nature with Computational Science for Optimal Signal Extraction

Hossein Hassani, Mohammad Reza Yeganegi and Xu Huang
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Hossein Hassani: Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran
Mohammad Reza Yeganegi: Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran
Xu Huang: Leicester Castle Business School, De Montfort University, Leicester LE1 9BH, UK

Stats, 2021, vol. 4, issue 1, 1-15

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.

Keywords: forecasting; Singular Spectrum Analysis; genetic algorithm (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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