Forecasting seasonal time series based on fuzzy techniques

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F19%3AA2001X62" target="_blank" >RIV/61988987:17610/19:A2001X62 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S016501141830678X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S016501141830678X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.fss.2018.09.010" target="_blank" >10.1016/j.fss.2018.09.010</a>

Result language
angličtina
Original language name
Forecasting seasonal time series based on fuzzy techniques
Original language description
This paper is devoted to a method for the forecasting of seasonal time series. The core of our approach is based on the fuzzy transform and fuzzy natural logic (FNL) techniques. Under the assumption that a time series can be additively decomposed into a trend-cycle, a seasonal component and an irregular fluctuation, the forecasting is a combination of individual forecasting of each of these constituents. More precisely, the trend-cycle and the seasonal component are predicted with the help of fuzzy transform, pattern recognition and fuzzy natural logic techniques. To model the irregular component, we apply the Box-Jenkins approach. In the paper, we compare the suggested method with two other well-known methods, namely STL and ARIMA ones.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10102 - Applied mathematics

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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