采用一种基于自编码器的异常检测算法, 实现大规模日线损率数据的异常检测. 变分自编码器是一种利用反向传播算法使得输出值近似等于输入值的神经网络, 使用自编码器将原始日线损率时间序列编码, 在重建过程中记录每个时间点的重建概率, 当重建概率大于指定阈值时就判定其为异常数据. 本文利用真实日线损数据进行实验, 试验结果表明, 基于自编码器的日线损率异常检测算法具有较好的检测效果.
This paper adopts an anomaly detection algorithm based on a self-encoder to achieve anomaly detection of large-scale daily line loss rate data. A variational auto-encoder is a neural network that uses the backpropagation algorithm to make the output value approximately equal to the input value. It uses the auto-encoder to encode the original daily line loss rate time series and records the reconstruction possibility at each time point during the reconstruction process. When the reconstruction possibility is greater than a specified threshold, it is classified as anomaly data. In this paper, experiments were conducted on real daily line loss data. The test results show that the proposed algorithm for abnormal detection of daily line loss rate data based on an auto-encoder has good detection capability.
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中国综合性科技类核心期刊(北大核心)