Segmentation Based Testing of Co-movement Significance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU129588" target="_blank" >RIV/00216305:26220/18:PU129588 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8422048" target="_blank" >https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8422048</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IWSSIP.2018.8439304" target="_blank" >10.1109/IWSSIP.2018.8439304</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Segmentation Based Testing of Co-movement Significance

Popis výsledku v původním jazyce

The paper is focused on the significance testing of the time-frequency co-movement measure on the segmentation bases. Investigating the test of the power wavelet cross-spectrum we have some standard assumptions: i.e two independent Gaussian white noise inputs. Then, with the use of the Bessel function, we can test whether the values of power wavelet cross-spectrum are significant with respect to the variance of each input time series. Our paper investigate the case when an input data have heteroscedastic character. Thus we propose firstly segmentation of the data sample according to the variances of input time series. Secondly, we propose an identification significant power wavelet cross-spectrum values in each segment via Ge test. The results with and without segmentation are compared. Our experiment is performed on simulated and real data. The results shows, that segmentation based testing for the heteroscedastic data provides more precise results.

Název v anglickém jazyce

Segmentation Based Testing of Co-movement Significance

Popis výsledku anglicky

The paper is focused on the significance testing of the time-frequency co-movement measure on the segmentation bases. Investigating the test of the power wavelet cross-spectrum we have some standard assumptions: i.e two independent Gaussian white noise inputs. Then, with the use of the Bessel function, we can test whether the values of power wavelet cross-spectrum are significant with respect to the variance of each input time series. Our paper investigate the case when an input data have heteroscedastic character. Thus we propose firstly segmentation of the data sample according to the variances of input time series. Secondly, we propose an identification significant power wavelet cross-spectrum values in each segment via Ge test. The results with and without segmentation are compared. Our experiment is performed on simulated and real data. The results shows, that segmentation based testing for the heteroscedastic data provides more precise results.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 25th International Conference on Systems, Signals and Image Processing

ISBN

978-1-5386-6979-2

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Neuveden

Místo vydání

Maribor

Místo konání akce

Maribor

Datum konání akce

20. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000451277200015