Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference

The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpret...
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Autor*in:	Peng He [verfasserIn] Ruishan Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	aviation incident trend analysis causal inference statistical inference Causal-ARIMA

Übergeordnetes Werk:	In: Aerospace - MDPI AG, 2014, 10(2023), 822, p 822
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:822, p 822

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/aerospace10090822

Katalog-ID:	DOAJ093476108

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520			\|a The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.
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allfieldsSound	10.3390/aerospace10090822 doi (DE-627)DOAJ093476108 (DE-599)DOAJ696da1b673ad4a2789c5b3be1534e94e DE-627 ger DE-627 rakwb eng TL1-4050 Peng He verfasserin aut Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management. aviation incident trend analysis causal inference statistical inference Causal-ARIMA Motor vehicles. Aeronautics. Astronautics Ruishan Sun verfasserin aut In Aerospace MDPI AG, 2014 10(2023), 822, p 822 (DE-627)778375048 (DE-600)2756091-0 22264310 nnns volume:10 year:2023 number:822, p 822 https://doi.org/10.3390/aerospace10090822 kostenfrei https://doaj.org/article/696da1b673ad4a2789c5b3be1534e94e kostenfrei https://www.mdpi.com/2226-4310/10/9/822 kostenfrei https://doaj.org/toc/2226-4310 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 822, p 822
language	English
source	In Aerospace 10(2023), 822, p 822 volume:10 year:2023 number:822, p 822
sourceStr	In Aerospace 10(2023), 822, p 822 volume:10 year:2023 number:822, p 822
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	aviation incident trend analysis causal inference statistical inference Causal-ARIMA Motor vehicles. Aeronautics. Astronautics
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container_title	Aerospace
authorswithroles_txt_mv	Peng He @@aut@@ Ruishan Sun @@aut@@
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callnumber-first	T - Technology
author	Peng He
spellingShingle	Peng He misc TL1-4050 misc aviation incident misc trend analysis misc causal inference misc statistical inference misc Causal-ARIMA misc Motor vehicles. Aeronautics. Astronautics Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference
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topic_title	TL1-4050 Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference aviation incident trend analysis causal inference statistical inference Causal-ARIMA
topic	misc TL1-4050 misc aviation incident misc trend analysis misc causal inference misc statistical inference misc Causal-ARIMA misc Motor vehicles. Aeronautics. Astronautics
topic_unstemmed	misc TL1-4050 misc aviation incident misc trend analysis misc causal inference misc statistical inference misc Causal-ARIMA misc Motor vehicles. Aeronautics. Astronautics
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title	Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference
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title_full	Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference
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title_sort	trend analysis of civil aviation incidents based on causal inference and statistical inference
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title_auth	Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference
abstract	The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.
abstractGer	The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.
abstract_unstemmed	The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.
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title_short	Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference
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