大气电场测量数据的异常检测及校正方法研究

Research on anomaly detection and correction method of atmospheric electric field measurement data

大气电场序列的清洗预处理对后续的挖掘研究具有重要意义。提出一种基于孤立森林结合Chen-Liu迭代算法的大气电场异常点检测与校正方法。该方法利用求和自回归移动平均(ARIMA)模型对大气电场时间序列进行拟合并得到拟合残差,基于残差序列构建孤立森林模型以确定异常点位置,最后通过Chen-Liu算法进行校正。通过模拟和实测大气电场数据验证所提方法的可靠性,相对于原序列,清洗后大气电场序列预测结果在均方根误差和平均百分比误差分别改善27.8%和34.98%。

The cleaning of the atmospheric electric field is the key step of pretreatment,which is of great significance to the subsequent excavation research.In view of the shortcomings of traditional anomaly detection algorithm,which needs to specify the corresponding parameters and fail to use the relevant information between time series,a new outlier detection and correction method based on the combination of isolation forest and Chen-Liu algorithm is proposed.The method uses ARIMA model to combine the atmospheric electric field to get the fitting residual.The isolation forest model is constructed based on residual sequence to determine the location of the outliers.Finally,the Chen-Liu algorithm is used to correct the outliers.The reliability of the proposed method is verified by simulation series and the atmospheric electric field test.Compared with the original prediction,the results of the prediction of the series of thr atmospheric electric field after cleaning are improved by 27.8%and 34.98%respectively in root mean square error and mean percentage error.