SEAD: Unsupervised Ensemble of Streaming Anomaly Detectors
Authors: Saumya Gaurang Shah, Abishek Sankararaman, Balakrishnan Murali Narayanaswamy, Vikramank Singh
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 14 non-trivial public datasets and an internal dataset corroborate our claims. |
| Researcher Affiliation | Industry | 1Amazon Web Services, Santa Clara, CA, USA. Correspondence to: Saumya Gaurang Shah <EMAIL>. |
| Pseudocode | Yes | The complete pseudo code is in Algorithm 1. Algorithm 1 SEAD Algorithm Algorithm 2 SEAD ++: Optimizing runtime by sampling |
| Open Source Code | No | The paper mentions using "open source implementations from Py SAD (Yilmaz & Kozat, 2020)" for base models but does not state that the code for SEAD itself is open-source, nor does it provide a link. |
| Open Datasets | Yes | We perform experiments on 15 datasets, of which 11 are from the Outlier Detection Data Sets (ODDS) (Rayana, 2016), 3 are from the USP Data Stream Repository (Souza et al., 2020) and one is an internal telemetry dataset from a multiserver database cloud service. |
| Dataset Splits | Yes | We set the first 100 data points for warm starting the base models and SEAD , but not for evaluation, i.e., is cold start . To overcome this issue, we split each dataset into chunks of 50 contiguous data points. |
| Hardware Specification | Yes | We performed all experiments on a single c5.2xlarge AWS EC2 instance. |
| Software Dependencies | No | The paper mentions using "open source implementations from Py SAD (Yilmaz & Kozat, 2020)" and "tdigest (Dunning & Ertl, 2019)" but does not provide specific version numbers for these or other software libraries/dependencies. |
| Experiment Setup | Yes | For our method SEAD , we choose hyperparameters η = 1, λ = 10 6 and π = Uniform distribution across all experiments. Table 11. Hyperparameter configurations for the base models. We set the first 100 data points for warm starting the base models and SEAD |