  Time series anomaly detection plays a vital role in a wide range of
applications. Existing methods require training one specific model for each
dataset, which exhibits limited generalization capability across different
target datasets, hindering anomaly detection performance in various scenarios
with scarce training data. Aiming at this problem, we propose constructing a
general time series anomaly detection model, which is pre-trained on extensive
multi-domain datasets and can subsequently apply to a multitude of downstream
scenarios. The significant divergence of time series data across different
domains presents two primary challenges in building such a general model: (1)
meeting the diverse requirements of appropriate information bottlenecks
tailored to different datasets in one unified model, and (2) enabling
distinguishment between multiple normal and abnormal patterns, both are crucial
for effective anomaly detection in various target scenarios. To tackle these
two challenges, we propose a General time series anomaly Detector with Adaptive
Bottlenecks and Dual Adversarial Decoders (DADA), which enables flexible
selection of bottlenecks based on different data and explicitly enhances clear
differentiation between normal and abnormal series. We conduct extensive
experiments on nine target datasets from different domains. After pre-training
on multi-domain data, DADA, serving as a zero-shot anomaly detector for these
datasets, still achieves competitive or even superior results compared to those
models tailored to each specific dataset.
