ImDiffusion：用于多变量时间序列异常检测的插补扩散模型
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
AI总结：
ImDiffusion 结合时间序列插补与扩散模型，利用邻近值和去噪输出实现多变量时间序列异常检测，在基准和微软生产系统中显著提升准确性和 F1 分数。
摘要：
多变量时间序列数据中的异常检测对于确保跨不同领域的大规模系统高效运行至关重要。然而，在此类数据中准确检测异常带来了重大挑战。现有方法，包括基于预测和基于重建的方法，难以有效应对这些挑战。为了克服这些局限性，我们提出了一种名为 ImDiffusion 的新型异常检测框架，该框架结合了时间序列插补和扩散模型，以实现准确且稳健的异常检测。ImDiffusion 采用的基于插补的方法利用了时间序列中邻近值的信息，能够精确建模时间依赖性和互相关依赖性，减少数据中的不确定性，从而增强异常检测过程的稳健性。ImDiffusion 进一步利用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，从而提高了检测过程的准确性和稳健性。我们通过在基准数据集上进行大量实验来评估 ImDiffusion 的性能。结果表明，我们提出的框架在检测准确性和及时性方面显著优于最先进的方法。ImDiffusion 进一步集成到微软的真实生产系统中，与旧方法相比，检测 F1 分数显著提高了 11.4%。据我们所知，ImDiffusion 代表了将基于插补的技术与时间序列异常检测相结合的开创性方法，同时将扩散模型的新颖应用引入该领域。
英文摘要：
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.
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