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.

本文介绍了一种用于时间序列中的无监督在线异常检测的混合注意力和自编码器（AE）模型。该模型结合了注意力和自编码器的方法进行时间序列异常检测，通过对自编码器潜在空间中的下一个时间步骤窗口进行预测，使用验证数据集中的阈值进行异常检测，并引入了基于错误的第一统计矩的替代方法，提高了准确性。通过对多样的真实世界基准数据集进行评估，并与其他成熟模型进行比较，验证了我们提出的模型在异常检测中的有效性。