Research map / Time series / Anomaly detection
Non-Parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add inter-series relatedness regularization (multi-task) to single-series smoothing (ablation) | non-parametric outlier detector · PMU network-level events | precision: 31.8 -> 94.8 | Helped |
| add inter-series relatedness regularization (ablation) | non-parametric outlier detector · PMU network-level events | recall: 19.2 -> 91.1 | Helped |
| add inter-series relatedness regularization (ablation) | non-parametric outlier detector · PMU simple outliers | precision: 84.4 -> 96.7 | Helped |
| add inter-series relatedness regularization (ablation) | non-parametric outlier detector · PMU simple outliers | recall: 92.8 -> 91.7 | No clear effect |
| use proposed non-parametric model instead of mARIMA | outlier detection · PMU network-level events | recall: 57.7 -> 91.1 | Helped |
| use proposed non-parametric model instead of CHMM (EM) | outlier detection · PMU network-level events | precision: 92.5 -> 94.8 | Helped |
| use RBCD instead of ADMM to solve the model | non-parametric model · training time for large training size | running time: at least 42.1% reduction | Helped |
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- Power Grid Anomaly Detection via Hybrid LSTM-GIN Model (Student Abstract) (AAAI 2024 · similarity 0.84)
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- KAN-AD: Time Series Anomaly Detection with Kolmogorov–Arnold Networks (ICML 2025 · similarity 0.82)
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