Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.

翻译：多变量时间序列数据中的异常检测对于确保跨领域大规模系统的高效运行至关重要。然而，准确检测此类数据中的异常面临重大挑战。现有方法（包括基于预测和重建的方法）难以有效应对这些挑战。为克服这些局限，我们提出一种名为ImDiffusion的新型异常检测框架，该框架融合时间序列插补与扩散模型，以实现准确且鲁棒的异常检测。ImDiffusion采用的基于插补的方法利用时间序列中邻近值的信息，能够精确建模时间依赖与互相关依赖，降低数据不确定性，从而增强异常检测过程的鲁棒性。ImDiffusion进一步将扩散模型作为时间序列插补器，以准确捕获复杂依赖关系。我们利用推理过程中生成的逐步去噪输出作为异常预测的有价值信号，提升检测过程的准确性与鲁棒性。通过在基准数据集上开展广泛实验，我们评估了ImDiffusion的性能。结果表明，所提框架在检测精度和时效性方面显著优于现有最先进方法。ImDiffusion已集成至微软实际生产系统中，与原有方法相比，检测F1分数提升了11.4%。据我们所知，ImDiffusion是首项将基于插补的技术与时间序列异常检测相结合，并创新性地将扩散模型引入该领域的研究工作。