SLAVÍČEK, Karel, Otto DOSTÁL and Vladimír SCHINDLER. DWY Time Series Decomposition Model. In International Conference on Software, Electronics & Industrial Engineering. Pattaya, Thailand: Planetary Scientific Research Centre, 2014, p. 27-30. ISBN 978-93-84468-10-1.
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Basic information
Original name DWY Time Series Decomposition Model
Authors SLAVÍČEK, Karel (203 Czech Republic, belonging to the institution), Otto DOSTÁL (203 Czech Republic, belonging to the institution) and Vladimír SCHINDLER (203 Czech Republic, guarantor, belonging to the institution).
Edition Pattaya, Thailand, International Conference on Software, Electronics & Industrial Engineering, p. 27-30, 4 pp. 2014.
Publisher Planetary Scientific Research Centre
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Thailand
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
RIV identification code RIV/00216224:14610/14:00073307
Organization unit Institute of Computer Science
ISBN 978-93-84468-10-1
Keywords in English time series; weekly cyccle; calendar dependency
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: doc. Mgr. Karel Slavíček, Ph.D., učo 1158. Changed: 7/4/2015 09:34.
Abstract
This paper describes a novel approach for time series decomposition suitable for time series with strong dependency on weekly cycle. There are many models of time series description. The most popular are trend + seasonal + cyclic component decomposition and the Box-Jenkins methodology. The big advantage of decomposition models is very intuitive mapping of real world into mathematical model. These models are easily understandable even to common users without deep mathematical background. On the other hand, current decomposition models are not so much suitable for modeling of complex time series with more periodic components and asymmetric behavior during the period. The model proposed in this paper better fits more deep structured time series as it respects the asymmetric behavior of the time series during all of its' periods.
Links
TA01010268, research and development projectName: Bezúdržbový PACS server pro menší zdravotnické organizace
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