PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection
This work addresses anomaly detection for real-time monitoring in streaming data from connected devices, but it appears incremental as it builds on existing reconstruction-based frameworks.
The authors tackled time series anomaly detection by introducing PatchTrAD, a patch-based Transformer model that uses reconstruction errors, and found it matches state-of-the-art deep learning models in detection performance while being time-efficient during inference.
Time series anomaly detection (TSAD) focuses on identifying whether observations in streaming data deviate significantly from normal patterns. With the prevalence of connected devices, anomaly detection on time series has become paramount, as it enables real-time monitoring and early detection of irregular behaviors across various application domains. In this work, we introduce PatchTrAD, a Patch-based Transformer model for time series anomaly detection. Our approach leverages a Transformer encoder along with the use of patches under a reconstructionbased framework for anomaly detection. Empirical evaluations on multiple benchmark datasets show that PatchTrAD is on par, in terms of detection performance, with state-of-the-art deep learning models for anomaly detection while being time efficient during inference.