CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series

TL;DR

CICADA introduces a novel cross-domain anomaly detection framework for multivariate time series that effectively handles data shifts, improves generalization, and enhances interpretability through innovative meta-learning, expert fusion, and adaptive expansion techniques.

Contribution

The paper presents CICADA, a new method combining mixture of experts, meta-learning, and hierarchical attention to improve cross-domain anomaly detection and interpretability.

Findings

Outperforms state-of-the-art methods in cross-domain detection accuracy.

Demonstrates robustness to data distribution shifts across domains.

Enhances interpretability of anomaly detection models.

Abstract

Unsupervised Time series anomaly detection plays a crucial role in applications across industries. However, existing methods face significant challenges due to data distributional shifts across different domains, which are exacerbated by the non-stationarity of time series over time. Existing models fail to generalize under multiple heterogeneous source domains and emerging unseen new target domains. To fill the research gap, we introduce CICADA (Cross-domain Interpretable Coding for Anomaly Detection and Adaptation), with four key innovations: (1) a mixture of experts (MOE) framework that captures domain-agnostic anomaly features with high flexibility and interpretability; (2) a novel selective meta-learning mechanism to prevent negative transfer between dissimilar domains, (3) an adaptive expansion algorithm for emerging heterogeneous domain expansion, and (4) a hierarchical attention structure that quantifies expert contributions during fusion to enhance interpretability further.Extensive experiments on synthetic and real-world industrial datasets demonstrate that CICADA outperforms state-of-the-art methods in both cross-domain detection performance and interpretability.