  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.
