Characteristic Analysis of Flight Delayed Time Series

In order to analyze the characteristics of airport flight delayed time series, based on the construction of flight delay time series, firstly, the K-means algorithm is used to cluster the time series of delayed departures. Secondly, combining with R/S analysis method of Fractal theory, Hurst index o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lan Ma, Shangheng Ou
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	delay characteristic analysis k-means algorithm fractal characteristics var 40 Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/d8343d85cde44923bac0a9f245907120
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d8343d85cde44923bac0a9f245907120
record_format	dspace
spelling	oai:doaj.org-article:d8343d85cde44923bac0a9f2459071202021-12-05T14:10:51ZCharacteristic Analysis of Flight Delayed Time Series2191-026X10.1515/jisys-2020-0045https://doaj.org/article/d8343d85cde44923bac0a9f2459071202020-12-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0045https://doaj.org/toc/2191-026XIn order to analyze the characteristics of airport flight delayed time series, based on the construction of flight delay time series, firstly, the K-means algorithm is used to cluster the time series of delayed departures. Secondly, combining with R/S analysis method of Fractal theory, Hurst index of the series is calculated, and Fractal characteristics of the series are analyzed. Then, the VAR (Vector Auto Regression) model is constructed, and Impulse Response Function (IRF) and Variance Decomposition are conducted to explore the impact of the fluctuation of flight delay time series on the future delay. The results show that K-means algorithm divides the time series into five categories, and each category has significant characteristics. Hurst index values of different time series are in the interval of (0.5, 1), indicating that the time series have good fractal characteristics. Through the IRF and Variance Decomposition of VAR model, results show that the time series are significantly affected by random pulses, and the prediction changes of the series come from multiple time series fluctuations. The prediction results show that the flight delay time series is predictable.Lan MaShangheng OuDe Gruyterarticledelay characteristic analysisk-means algorithmfractal characteristicsvar40ScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 361-375 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	delay characteristic analysis k-means algorithm fractal characteristics var 40 Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	delay characteristic analysis k-means algorithm fractal characteristics var 40 Science Q Electronic computers. Computer science QA75.5-76.95 Lan Ma Shangheng Ou Characteristic Analysis of Flight Delayed Time Series
description	In order to analyze the characteristics of airport flight delayed time series, based on the construction of flight delay time series, firstly, the K-means algorithm is used to cluster the time series of delayed departures. Secondly, combining with R/S analysis method of Fractal theory, Hurst index of the series is calculated, and Fractal characteristics of the series are analyzed. Then, the VAR (Vector Auto Regression) model is constructed, and Impulse Response Function (IRF) and Variance Decomposition are conducted to explore the impact of the fluctuation of flight delay time series on the future delay. The results show that K-means algorithm divides the time series into five categories, and each category has significant characteristics. Hurst index values of different time series are in the interval of (0.5, 1), indicating that the time series have good fractal characteristics. Through the IRF and Variance Decomposition of VAR model, results show that the time series are significantly affected by random pulses, and the prediction changes of the series come from multiple time series fluctuations. The prediction results show that the flight delay time series is predictable.
format	article
author	Lan Ma Shangheng Ou
author_facet	Lan Ma Shangheng Ou
author_sort	Lan Ma
title	Characteristic Analysis of Flight Delayed Time Series
title_short	Characteristic Analysis of Flight Delayed Time Series
title_full	Characteristic Analysis of Flight Delayed Time Series
title_fullStr	Characteristic Analysis of Flight Delayed Time Series
title_full_unstemmed	Characteristic Analysis of Flight Delayed Time Series
title_sort	characteristic analysis of flight delayed time series
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/d8343d85cde44923bac0a9f245907120
work_keys_str_mv	AT lanma characteristicanalysisofflightdelayedtimeseries AT shanghengou characteristicanalysisofflightdelayedtimeseries
_version_	1718371692424724480

Characteristic Analysis of Flight Delayed Time Series

Ejemplares similares