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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.
Highlights
IntroductionThe vigorous advancements of data science and computational technologies recent decades have significantly altered the way of conducting interdisciplinary research
We used a set of 100 real time series, with different sampling frequencies, normality, stationarity and skewness characteristics, to compare the accuracy of Singular Spectrum Analysis (SSA)-genetic algorithm (GA) whit basic
This includes skewness statistics, results from the normality (Shapiro-Wilk) and stationarity (Augmented Dickey-Fuller) tests. As it can be seen the data comes from different fields of energy, finance, health, tourism, housing market, crime, agriculture, economics, chemistry, ecology, and production, to name a few
Summary
The vigorous advancements of data science and computational technologies recent decades have significantly altered the way of conducting interdisciplinary research. These interdisciplinary developments have injected novel aspects of thinking and problem solving capabilities back to the progression of computational algorithms. Just as the phrase “let nature take its course”, researchers seek means to better appreciate the solutions nature may have to offer. It is not new that researchers invent and implement algorithms inspired by the nature as intelligent solution to complex problems and these achievements continuously bring new breakthroughs on a wider scale of science and technology. A recent review focusing on nature inspired algorithms can be found in [1]. Some well established models include: the neural networks [2], which was inspired by the mechanism of biological neural networks, and has been widely applied and developed to form a large branch containing various types of computational architectures; swarm intelligence (SI) [3,4], which has been contributing to the intelligent advancements on both scientific and engineering domains, and a wide spectrum of SI inspired algorithms (i.e., bat algorithm, ant colony optimization, firefly algorithm, etc.)
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