ADAPTIVE AND SCALABLE APPROACH FOR TIME SERIES ANOMALY DETECTION USING TRANSFORMER

This paper introduces an innovative approach to time series anomaly detection, focusing on addressing the challenge of adapting to concept drift within input time series. Traditional time series anomaly detection methods often assume a stable environment for modeling. However, in practical applications, time series data may be influenced by concept drift, where the data distribution and features change over time. To effectively tackle this issue, we propose a dynamic adaptive model capable of real-time detection and adaptation to concept drift within time series data. Our algorithm leverages advanced time series anomaly detection techniques, particularly a Transformer-based online learning model, utilizing prediction errors as the basis for anomaly scores. Notably, the model incorporates a mechanism for concept drift detection and adaptation, continuously monitoring changes in the time series to adjust the learning rate accordingly. Additionally, our approach enhances the self-attention mechanism within the Transformer model by employing sparse self-attention, improving computational efficiency. Experimental results demonstrate that our algorithm exhibits superior accuracy in the presence of concept drift compared to traditional time series anomaly detection methods. This makes our approach particularly valuable in practical applications, especially in dynamic environments requiring adaptability. The combination of online learning, concept drift adaptation, and optimized self-attention contributes to the algorithm's effectiveness in handling real-world time series data.