Noise-contrastive Online Change Point Detection
About
We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to flexible algorithms suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets.
Nikita Puchkin, Artur Goldman, Konstantin Yakovlev, Valeriia Dzis, Uliana Vinogradova• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Change Point Detection | Room Occupancy (test) | False Alarm Rate1 | 9 | |
| Change Point Detection | CENSREC-1-C Clean Record | FA Rate0.00e+0 | 9 | |
| Change Point Detection | CENSREC-1-C SNR 20 | FA0.00e+0 | 9 | |
| Change Point Detection | CENSREC-1-C SNR 15 | FA0.00e+0 | 9 | |
| Change Point Detection | Synthetic data Example 1 | Detection Delay5.9 | 5 | |
| Change Point Detection | Synthetic data Example 1 (test) | FA Rate0.00e+0 | 5 | |
| Change Point Detection | Synthetic data Example 2 (test) | FA0.00e+0 | 5 | |
| Change Point Detection | Synthetic data Example 3 (test) | FA Rate0.00e+0 | 5 | |
| Change Point Detection | Synthetic data Example 4 (test) | False Alarm Rate0.00e+0 | 5 | |
| Human activity detection | WISDM | FA Rate1 | 5 |
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