mlsanityData to 14 Oct 2026

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

arXiv 2505.00415 ↗First posted 1 May 2025cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 1 May 2025.

Builds on

17 of 32 references.

References · 17

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 763
  3. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  4. 2019Which Tasks Should Be Learned Together in Multi-task Learning?cited by 375
  5. 2021Efficiently Identifying Task Groupings for Multi-Task Learningcited by 222
  6. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  7. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  8. 2019Feature-Critic Networks for Heterogeneous Domain Generalizationcited by 130
  9. 2021Open Domain Generalization with Domain-Augmented Meta-Learningcited by 115
  10. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  11. 2019Hierarchically Structured Meta-learningcited by 68
  12. 2021Domain Adaptation for Time Series Forecasting via Attention Sharingcited by 68
5 more
  1. 2020Time Series Domain Adaptation via Sparse Associative Structure Alignmentcited by 51
  2. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  3. 2022Domain Adaptation for Time-Series Classification to Mitigate Covariate Shiftcited by 24
  4. 2021CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learningcited by 23
  5. 2024More is Better: Deep Domain Adaptation with Multiple Sourcescited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancycited by 10
  2. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Modelscited by 4
  3. 2026Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detectioncited by 0

Similar, not cited

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  1. 2026COMET: Codebook-based Online-adaptive Multi-scale Embedding for Time-series Anomaly Detection
  2. 2025RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
  3. 2026Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
  4. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
  5. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers
  6. 2025Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning
  7. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
  8. 2024Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection