Innovative Approach to Wind Direction Data Analyses: A Compositional Periodic Spline Representation in Bayes Spaces

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923641" target="_blank" >RIV/00216275:25410/25:39923641 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15310/25:73634592

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-92383-8_41" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-92383-8_41</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-92383-8_41" target="_blank" >10.1007/978-3-031-92383-8_41</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Innovative Approach to Wind Direction Data Analyses: A Compositional Periodic Spline Representation in Bayes Spaces

Popis výsledku v původním jazyce

Analyzing environmental data within their natural sample space is essential for optimizing energy, economics, and transport systems. Wind direction, for example, significantly impacts aircraft landing, power transmission, and wind energy generation. However, its circular nature presents unique challenges. Previous research on wind direction distribution has employed methods such as Newton&apos;s optimization of von Mises distribution mixtures, the EM algorithm, and evolutionary algorithms, focusing primarily on the approximation of individual functional observations. In this study, we advanced this field by introducing a compositional periodic spline representation of wind direction data within the Bayes spaces framework. The presented approach is efficient for processing directional data using functional data analysis techniques. Our theoretical framework will be validated on empirical wind direction datasets.

Název v anglickém jazyce

Innovative Approach to Wind Direction Data Analyses: A Compositional Periodic Spline Representation in Bayes Spaces

Popis výsledku anglicky

Analyzing environmental data within their natural sample space is essential for optimizing energy, economics, and transport systems. Wind direction, for example, significantly impacts aircraft landing, power transmission, and wind energy generation. However, its circular nature presents unique challenges. Previous research on wind direction distribution has employed methods such as Newton&apos;s optimization of von Mises distribution mixtures, the EM algorithm, and evolutionary algorithms, focusing primarily on the approximation of individual functional observations. In this study, we advanced this field by introducing a compositional periodic spline representation of wind direction data within the Bayes spaces framework. The presented approach is efficient for processing directional data using functional data analysis techniques. Our theoretical framework will be validated on empirical wind direction datasets.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10100 - Mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

New Trends in Functional Statistics and Related Fields

ISBN

978-3-031-92382-1

ISSN

1431-1968

e-ISSN

—

Počet stran výsledku

8

Strana od-do

337-344

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Novara

Datum konání akce

25. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001545850800041