This paper proposed a new method for identification of ionospheric TEC anomalies using prophet forecasting model based on Facebook. First, we compared the precision of this new method with the traditional time series forecasting method (Autoregressive Integrated Moving Average, ARIMA models) and the identification method of the classical ionospheric TEC anomalies (Inter Quartile Range, IQR method), in predicting the background values of ionospheric TEC modeling. The results show that the precision of the former is obviously better than the latter two methods: about 2.55 times higher than that of the ARIMA models, and about 10.74 times higher than that of the IQR method. Meanwhile, when the best prediction modeling interval is established, the comparison of precision values is RMSEIQR=10.5841>RMSEARIMA=3.2780>RMSEProphet=0.846, indicating that the traditional detection methods have insufficiency in predicting modeling background values. Second, taking the August 8, 2017 Jiuzhaigou earthquake as example, we analyzed its ionosphere TEC anomalies and proved the effectiveness and accuracy of the new method. The results show that obvious ionosphere TEC negative anomalies appeared on the 10 th and 2 nd days before the earthquake, and obvious ionosphere TEC positive anomalies occurred on the 7 th day before the earthquake. In addition, the comparative experiments show that the validity and accuracy of the Prophet forecasting model is significantly better than the IQR method.%提出一种基于Facebook开源的Prophet预测模型进行电离层TEC异常识别的新方法.首先,对比分析了该方法与传统时间序列预测方法(ARIMA模型等)预测电离层TEC建模背景值的精度,以及与经典电离层TEC异常识别方法(滑动四分位法)提取前面对应一致的电离层TEC背景值的精度.结果表明,Prophet预测模型预测建模背景值的精度要明显优于其他方法,且预测的建模精度比ARIMA模型等方法高2.55倍左右,比滑动四分位法高10.74倍左右.同时,在最佳预测建模区间时,其精度值大小比较依次为RMSEIQR=10.5841>RMSEARIMA=3.2780>RMSEProphet=0.8469,说明传统探测法预测建模背景值时具有较大的不足.随后,以2017年8月8日九寨沟7.0级地震为例,利用该方法分析了电离层TEC异常扰动情况,并对比验证了该方法的有效性和准确性.实验结果表明:在震前第10 d和第2 d电离层TEC发生较为明显的负异常,第7 d电离层TEC发生较为明显的正异常.对比实验表明,Prophet预测模型的有效性和准确性明显优于滑动四分位法.
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