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应用季节性差分自回归移动平均模型(SARIMA)对X航空公司动态安全指数进行预测分析,为航空公司运营安全的规划和发展提供决策依据。收集整理X航空公司2005年1—6月的安全事件和运行数据。利用动态安全指数计算方法对数据进行预处理,建立时间序列。应用SPSS软件对动态安全指数的时间序列进行模型拟合,建立SARIMA模型。对所获得的模型进行参数检验,选取最优模型。利用最优模型对2015年7—12月动态安全指数进行预测,并对预测值与实际值进行对比分析。结果表明,SARIMA(1,0,2)(0,1,0)12模型在显著性水平0.05下通过了所有参数检验。各月实际值都落入了拟合值95%的可信区间范围,动态安全指数的实际值与拟合值变化趋势基本一致。2010年之后精度较高,实际值与拟合值具有较好的重合度。△ln Yt拟合值的最大绝对误差为1.976 6(2009年12月),最小绝对误差为0.000 4(2013年9月)。2015年7—12月,动态安全指数的实际值与预测值变化趋势基本一致,但误差较大。SARIMA模型能够较好地短期模拟X航空公司动态安全状况和趋势,预测效果良好。当发生事故、严重事故征候时,序列的实际值会偏离序列原有的结构,预测精度下降。
The seasonal dynamic autoregressive moving average model (SARIMA) is used to predict and analyze the dynamic safety index of X-airlines, which can provide decision-making basis for the planning and development of airline’s operational safety. Collect and organize X-Line’s January-June 2005 security incident and operational data. Using the dynamic safety index calculation method to preprocess the data to establish the time series. Apply SPSS software to model the time series of dynamic safety index and establish SARIMA model. The parameters obtained by the model test, select the best model. The optimal model was used to predict the dynamic safety index from July to December in 2015, and the predicted and actual values were compared. The results show that the SARIMA (1,0,2) (0,1,0) 12 model passed all parameter tests at a significance level of 0.05. The actual value of each month falls within the confidence interval of 95% of the fitted value, and the actual value of the dynamic safety index is basically consistent with the trend of fitting value. After 2010, the precision is higher, and the actual value and the fit value have better coincidence degree. The maximum absolute error for the △ ln Yt fit is 1.976 6 (December 2009) and the minimum absolute error is 0.000 4 (September 2013). From July to December 2015, the trend of the actual value of the dynamic safety index is basically consistent with the forecast value, but the error is larger. The SARIMA model can simulate the dynamic and safety status and trend of X Airlines in a short period of time and the forecasting effect is good. When an accident or a serious incident occurs, the actual value of the sequence will deviate from the original structure of the sequence, and the prediction accuracy decreases.