Label-Efficient Interactive Time-Series Anomaly Detection

by   Hong Guo, et al.
Tsinghua University
Peking University
University of Washington

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.


page 1

page 2

page 3

page 4


Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection

Key Performance Indicators (KPI), which are essentially time series data...

RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning

We introduce a new semi-supervised, time series anomaly detection algori...

Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

Every year, criminals launder billions of dollars acquired from serious ...

Unsupervised Prediction of Negative Health Events Ahead of Time

The emergence of continuous health monitoring and the availability of an...

CRATOS: Cogination of Reliable Algorithm for Time-series Optimal Solution

Anomaly detection of time series plays an important role in reliability ...

Learning Time Series Detection Models from Temporally Imprecise Labels

In this paper, we consider a new low-quality label learning problem: lea...

Anomaly Detection in Audio with Concept Drift using Adaptive Huffman Coding

In this work, we propose a framework to apply Huffman coding for anomaly...

Please sign up or login with your details

Forgot password? Click here to reset