STGAD: Inference Throughput and Latency for Anomaly Detection

STGAD inference throughput and latency data is a small dataset of 5.5 KB in XLS format. It was authored by Xiao Liao and last updated on May 21, 2026. The data likely contains performance metrics for a dual-score generative-adversarial framework for anomaly detection in multivariate time series.

Use Cases

• Benchmarking model inference speed based on the described throughput and latency metrics
• Evaluating the computational efficiency of adversarial learning frameworks for time-series anomaly detection
• Comparing the performance of the STGAD framework's dual-score fusion mechanism against other methods

Strengths

• Dataset is small at 5.5 KB, facilitating quick download and inspection.
• Released under a permissive CC-BY-4.0 license, allowing for broad reuse and modification.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Xiao Liao

Freshness

Last updated 2026-05-21 17:42:19; freshness should be verified.