Harnessing Forecast Uncertainty in Deep Learning for Time Series Anomaly Detection with Posterior Distribution Scoring

Time series anomaly detection tools (TSAD) are widely applicable across industries, such as monitoring time series data of water pipeline pressure, network traffic activities, and hardware telemetry. The primary objective is to identify anomalous segments and alert users to potential issues before any consequences. A closely related tool is time series forecasting, and some practitioners leverage it for anomaly detection. As the main objective of a forecasting model is to minimize errors, it tends to over-fit the time series, making it challenging to distinguish whether the forecasting errors occur due to model limitations or true anomalous segments. This paper introduces a method called the posterior anomaly scoring criterion, which uses deep learning time series forecasting models to estimate forecast uncertainties for TSAD. We propose replacing the forecasting model’s output layer to estimate forecast distributions and compute the probability of the posterior distribution to attain anomaly scores. These scores are processed through an automated threshold criterion to classify the anomalous segments. The experiments have demonstrated our model performs the best in four out of five datasets across seven benchmark models.