Multi-instance-learning Based Time Series Outlier Detection Algorithm

On the basis of the FIndCBLOf algorithm, this paper proposes a FindCBLOF outlier detection algorithm (MIL-FindCBLof) for Multi instance learning based the detection of time series outliers. Firstly, the data were gathered using piecewise multi instance framework to package each object in order to preserve the properties for each object, to calculate the object factor with global strategy, to determine the outlier sequence by calculating the average factor. In the real-time monitoring system of practical enterprise MIL-FindCBLof algorithm will be compared with other classical outlier detection algorithm by experiments.The results show that the proposed MIL-FindCBLof algorithm compared with other algorithms in this paper can improve the accuracy and comprehensiveness of detection.