Automated Model Selection for Time-Series Anomaly Detection

TL;DR

This paper introduces an automated framework for selecting and tuning anomaly detection models for time-series data, addressing challenges of unlabeled, complex signals in industrial applications.

Contribution

It proposes an extensible model selection framework with a flexible tuning algorithm, improving adaptability and effectiveness in real-world time-series anomaly detection.

Findings

Effective model selection on real-world datasets

Flexible anomaly filtering tailored to customer needs

Extensible framework accommodates new detectors easily

Abstract

Time-series anomaly detection is a popular topic in both academia and industrial fields. Many companies need to monitor thousands of temporal signals for their applications and services and require instant feedback and alerts for potential incidents in time. The task is challenging because of the complex characteristics of time-series, which are messy, stochastic, and often without proper labels. This prohibits training supervised models because of lack of labels and a single model hardly fits different time series. In this paper, we propose a solution to address these issues. We present an automated model selection framework to automatically find the most suitable detection model with proper parameters for the incoming data. The model selection layer is extensible as it can be updated without too much effort when a new detector is available to the service. Finally, we incorporate a customized tuning algorithm to flexibly filter anomalies to meet customers' criteria. Experiments on real-world datasets show the effectiveness of our solution.