arXiv (All)
open access
CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation
Anomalous time series play a critical role in safety-critical domains, yet they are inherently scarce, heterogeneous, and costly to obtain. Existing time series generation methods predominantly focus on synthesizing normal data, providing limited value when anomalous samples are needed. We identify
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection#14030
- Anomaly-Preference Image Generation#1069792
- Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative#1000082
- Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study#14029
- Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability#1169667
Record · ID 1064369
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive