Fusing Nature with Computational Science for Optimal Signal Extraction
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 o...
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| Published in | Stats (Basel, Switzerland) Vol. 4; no. 1; pp. 71 - 85 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.03.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2571-905X 2571-905X |
| DOI | 10.3390/stats4010006 |
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| 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. |
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| AbstractList | 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. |
| Author | Hassani, Hossein Huang, Xu Yeganegi, Mohammad Reza |
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| SubjectTerms | Chromosomes Crude oil prices Decomposition Eigenvalues Forecasting genetic algorithm Genetic algorithms Interdisciplinary aspects Neural networks Noise Operations management Optimization Population Principal components analysis Singular Spectrum Analysis Spectrum analysis Time series Tourism |
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