• 论文 •

### 基于最近邻分析的空气质量时空数据异常点识别

• 出版日期:2017-08-15 发布日期:2017-08-25

### Outlier Detection from Air Quality Spatio-temporal Data Based on Nearest Neighbor Analysis

Nie Bin et al.

• Online:2017-08-15 Published:2017-08-25

Abstract: Air quality issues have received worldwide attention in recent years. Due to the continuous and spatial features of the air quality data, the outlier detection becomes much difficult. In this paper, the residual errors are predicted and calculated by applying the moving average method in the time dimension and the inverse distance weighted method in the space dimension, so that outlier detection from the spatio-temporal data can be transformed into outlier detection from the two-dimensional residual error value. In the two dimensions of the residual error value, the intensity of anomaly of each point to multiple neighboring points is calculated by the nearest neighbor analysis. The outlier can be defined when the probability of the intensity of anomaly greater than the threshold value exceeds the predetermined value. The simulation results show that the new method has a strong detection power. At the end, a satisfactory result for outlier detection is achieved while the real observation data set are applied with this new method.