Aviation Accident Prediction Based on Auto-Regressive Integrating Moving Average Method

Xu Sheng Gan; Jing Shun Duanmu; Jian Guo Gao

doi:10.4028/www.scientific.net/AMR.739.754

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 739Aviation Accident Prediction Based on...

Aviation Accident Prediction Based on Auto-Regressive Integrating Moving Average Method

Abstract:

In order to reinforce the efficiency and initiative of accident prevention for reducing accident loss, the surest way is to strengthen the prediction. In view of difficulties in aviation accident prediction, a prediction method based on Auto-Regressive Integrating Moving Average (ARIMA) is proposed. In this method, a trial model is first identified for an accident time series, and then the diagnosis is executed with the necessary adjustments. The calculation process including identification, estimation and diagnosis is repeated until obtain a satisfactory model. The example shows that, ARIMA has a good prediction for aviation accident and provides an important technique support for decision-making of aviation safety.

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Periodical:

Advanced Materials Research (Volume 739)

Pages:

754-758

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.739.754

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Online since:

August 2013

Authors:

Xu Sheng Gan, Jing Shun Duanmu, Jian Guo Gao

Keywords:

ADF Test, Auto-Regressive Integrating Moving Average, Aviation Accident, Flight Accident 10000-Hour-Rate

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References

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