Outlier detection in time series through neural networks forecasting
基于集成预测的稀有时间序列检测*

From the view of forecasting,a novel definition of outlier in time series was presented,as well as the definition of the forecasting confidence and the degree of outlier.The technique of permutation was proposed to alleviate the impact of out-liers upon the forecasting model.To solve the false alarm problem,the forecasting-based outlier detection algorithm was presented.The experiments conducted on the real-world datasets show that definition of the degree of outlier is reasonable and the outlier detection algorithm is effective.