Abstract: Most of the stream data anomaly detection algorithms employ a sliding single-window model, which leads to redundant calculations for a large number of data points and disturbs anomaly points due to the replacement of neighbors in the sliding window, thereby affecting the accuracy of anomaly detection algorithms. To address these issues, a combined window model is proposed, which utilizes several non-overlapping windows as the detection range for anomaly points. Based on this model, an anomaly detection algorithm based on grid density accumulation is introduced. Firstly, the kernel density estimation function is optimized and used to calculate the local density of data points. Then, a grid density accumulation operation is proposed to measure anomalous grids. In anomalous grids, the final anomalous data is determined by calculating the anomaly scores of data points. To improve the algorithm's efficiency, an adaptive pruning strategy is proposed to prune areas where anomaly points are unlikely to appear. Experimental results show that this algorithm exhibits significant advantages in both efficiency and accuracy compared to existing stream data anomaly detection algorithms.

Key words: anomaly detection, stream data, kernel density estimation, grid density stacking