Abstract: This paper presents an outlier pattern detection algorithm of time series based on K-Mean Distance Outlier Factor （K-MDOF）.This algorithm uses edge weight to extract the edge point of time series pattern representation，and then this algorithm extracts the four eigenvalue of each sub-pattern，that is，pattern’s length，pattern’s height，pattern’s mean and standard deviation to map time series to feature space，and finally uses K-mean distance outlier factor to detect outlier pattern in this feature space.Detecting time series’s outlier behavior from pattern’s point of view can recuperate the limitation of point outlier detection’s individual behavior，and enhance the efficiency and veracity of outlier detection.Experiments on synthetic and real data show that the definition of pattern outlier is reasonable and this algorithm is efficient to detect outliers in time series.