Deep Baseline Network for Time Series Modeling and Anomaly Detection

TL;DR

This paper introduces Deep Baseline Network (DBLN), a deep learning architecture designed to accurately extract baselines from time series data, enabling reliable and interpretable anomaly detection in various real-world applications.

Contribution

The paper presents a novel deep architecture, DBLN, that effectively extracts baselines from time series, improving anomaly detection accuracy without requiring labels.

Findings

Outperforms state-of-the-art methods on synthetic datasets

Achieves superior results on real-world datasets

Provides interpretable anomaly detection results

Abstract

Deep learning has seen increasing applications in time series in recent years. For time series anomaly detection scenarios, such as in finance, Internet of Things, data center operations, etc., time series usually show very flexible baselines depending on various external factors. Anomalies unveil themselves by lying far away from the baseline. However, the detection is not always easy due to some challenges including baseline shifting, lacking of labels, noise interference, real time detection in streaming data, result interpretability, etc. In this paper, we develop a novel deep architecture to properly extract the baseline from time series, namely Deep Baseline Network (DBLN). By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result. Empirical evaluation on both synthetic and public real-world datasets shows that our purely unsupervised algorithm achieves superior performance compared with state-of-art methods and has good practical applications.