  Time-series anomaly detection is an important task and has been widely
applied in the industry. Since manual data annotation is expensive and
inefficient, most applications adopt unsupervised anomaly detection methods,
but the results are usually sub-optimal and unsatisfactory to end customers.
Weak supervision is a promising paradigm for obtaining considerable labels in a
low-cost way, which enables the customers to label data by writing heuristic
rules rather than annotating each instance individually. However, in the
time-series domain, it is hard for people to write reasonable labeling
functions as the time-series data is numerically continuous and difficult to be
understood. In this paper, we propose a Label-Efficient Interactive Time-Series
Anomaly Detection (LEIAD) system, which enables a user to improve the results
of unsupervised anomaly detection by performing only a small amount of
interactions with the system. To achieve this goal, the system integrates weak
supervision and active learning collaboratively while generating labeling
functions automatically using only a few labeled data. All of these techniques
are complementary and can promote each other in a reinforced manner. We conduct
experiments on three time-series anomaly detection datasets, demonstrating that
the proposed system is superior to existing solutions in both weak supervision
and active learning areas. Also, the system has been tested in a real scenario
in industry to show its practicality.
