SCConv-Denoising Diffusion Probabilistic Model Anomaly Detection Based on TimesNet

Abstract

Time series anomaly detection is a significant challenge due to the inherent complexity and diversity of time series data. Traditional methods for time series anomaly detection (TAD) often struggle to effectively address the intricate nature of a complex time series and the composite characteristics of diverse anomalies. In this paper, we propose SCConv-Denoising Diffusion Probabilistic Model Anomaly Detection Based on TimesNet (SDADT), a novel framework that integrates the Spatial and Channel Reconstruction Convolution (SCConv) module and Denoising Diffusion Probabilistic Models (DDPMs) to address these challenges. By transforming 1D time series into 2D tensors via TimesNet, our method captures intra- and inter-period variations, achieving state-of-the-art performance across three real-world datasets: 85.39% F1-score on SMD, 92.76% on SWaT, and 97.36% on PSM, outperforming nine baseline models including Transformers and LSTM. Ablation studies confirm the necessity of both modules, with performance dropping significantly when either SCConv or DDPMs are removed. In conclusion, this paper proposes a novel alternative solution for anomaly detection in the Cyber Physical Systems (CPSs) domain.