用于无监督在线异常检测的注意力与自编码器混合模型
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection
AI总结：
本文提出一种结合注意力机制与自编码器的混合模型，用于时间序列无监督在线异常检测，通过潜在空间预测及误差一阶统计矩分析提升检测准确率。
摘要：
本文提出了一种用于时间序列无监督在线异常检测的注意力与自编码器（AE）混合模型。自编码器在短嵌入中捕获局部结构模式，而注意力模型则学习长期特征，并通过位置编码促进并行计算。该方法独特地首次在时间序列异常检测中结合了注意力机制和自编码器。它采用类似于深度Transformer模型的基于注意力的机制，并对架构进行了关键修改，以便在自编码器的潜在空间中预测下一个时间步窗口。该模型利用来自验证数据集的阈值进行异常检测，并引入了一种基于分析误差一阶统计矩的替代方法，在无需依赖验证数据集的情况下提高了准确性。在多样化的真实世界基准数据集上进行评估并与其他成熟模型进行比较，证实了本文所提模型在异常检测中的有效性。
英文摘要：
This paper introduces a hybrid attention and autoencoder (AE) model for unsupervised online anomaly detection in time series. The autoencoder captures local structural patterns in short embeddings, while the attention model learns long-term features, facilitating parallel computing with positional encoding. Unique in its approach, our proposed hybrid model combines attention and autoencoder for the first time in time series anomaly detection. It employs an attention-based mechanism, akin to the deep transformer model, with key architectural modifications for predicting the next time step window in the autoencoder's latent space. The model utilizes a threshold from the validation dataset for anomaly detection and introduces an alternative method based on analyzing the first statistical moment of error, improving accuracy without dependence on a validation dataset. Evaluation on diverse real-world benchmark datasets and comparing with other well-established models, confirms the effectiveness of our proposed model in anomaly detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
