  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.
