Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling
arXiv:2604.14221v2 Announce Type: replace Abstract: Reliable evaluation of anomaly detection methods in multivariate time series remains an open challenge, largely due to the limitations of existing benchmark datasets. Current resources often lack fine-grained anomaly annotations, do not provide explicit intervariable and temporal dependencies, and offer little insight into the underlying generative mechanisms. These shortcomings hinder the development and rigorous comparison of detection models, especially those targeting interpretable and variable-specific outputs. To address this gap, we in
The increasing sophistication and widespread application of AI systems necessitate more robust and interpretable anomaly detection methods, driving demand for better synthetic data generation.
Improved time series generation with fine-grained anomaly labeling will accelerate the development and reliability of AI models crucial for various industries, from finance to industrial IoT, by enabling more rigorous testing.
The ability to create more realistic and annotated benchmark datasets for multivariate time series anomaly detection changes how AI models are developed, evaluated, and improved.
- · AI researchers
- · Anomaly detection software developers
- · Industries relying on predictive maintenance
- · Companies relying on outdated anomaly detection methods
More accurate and interpretable anomaly detection models become available across various domains.
Reduced operational downtime and improved efficiency in systems where anomalies can be quickly and accurately identified.
Enhanced trust and adoption of AI systems in critical infrastructure and financial sectors due to improved reliability.
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Read at arXiv cs.AI