Time series analysis using soft computing methods

Autor:	Anton Romanov, Tatiana Afanasieva, Nadezhda Yarushkina, Irina Perfilieva
Rok vydání:	2013
Předmět:	Soft computing Series (mathematics) Computer science Residual computer.software_genre Fuzzy logic Computer Science Applications Theoretical Computer Science Order of integration Term (time) Control and Systems Engineering Modeling and Simulation Decomposition (computer science) Data mining Time series computer Algorithm Information Systems
Zdroj:	International Journal of General Systems. 42:687-705
ISSN:	1563-5104 0308-1079
DOI:	10.1080/03081079.2013.798911
Popis:	The aim of this study is to show that the integration of two soft computing techniques, namely the F-transform and fuzzy tendency modeling, can be successfully used in the analysis and forecasting of time series. The proposed method is based on the two-term additive decomposition of a time series, in which the first term is a low-frequency trend (expressed using direct F-transform components), and the second term is a residual vector that is processed as a stationary time series. A theoretical justification is given, and experiments are included. A practical application that shows the analysis of a time series with economic indicators is demonstrated.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f850cbdd175cf0c9136acff0514a2820 https://doi.org/10.1080/03081079.2013.798911 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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