Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unsupervised Model Selection for Time Series Anomaly Detection
Authors: Mononito Goswami, Cristian Ignacio Challu, Laurent Callot, Lenon Minorics, Andrey Kan
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Large-scale experiments on multiple real-world datasets demonstrate that our proposed unsupervised approach is as effective as selecting the most accurate model based on partially labeled data. |
| Researcher Affiliation | Collaboration | Mononito Goswami & Cristian Challu Carnegie Mellon University EMAIL Laurent Callot, Lenon Minorics & Andrey Kan Amazon Research EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods in prose and explains algorithms conceptually but does not include any formal pseudocode blocks or clearly labeled algorithm figures. |
| Open Source Code | Yes | We provide an open-source implementations of our model selection method and anomaly detection models at https://github.com/mononitogoswami/tsad-model-selection. |
| Open Datasets | Yes | All datasets used in this work are available in the public domain. Directions to download the Server Machine Dataset are available at https://github.com/Net Man AIOps/Omni Anomaly, whereas the UCR Anomaly Archive can be downloaded from https://www.cs.ucr.edu/ eamonn/time_series_data_2018/. |
| Dataset Splits | Yes | Specifically, we randomly partition each dataset into selection (20%) and evaluation (80%) sets. Each time-series in the dataset is assigned to one of these sets. The baseline method selects a model based on anomaly labels in the selection set. |
| Hardware Specification | Yes | All our experiments were carried out on an AWS g3.4xlarge EC2 instance with with 16 Intel(R) Xeon(R) CPUs, 122 Gi B RAM and a 8 Gi B GPU. |
| Software Dependencies | Yes | All models and model selection algorithms were trained and built using scikit-learn 1.1.1 (Pedregosa et al., 2011), Py Torch 1.11.0 Paszke et al. (2019) along with Python 3.9.13. |
| Experiment Setup | Yes | We created a pool of 3 k-NN (Ramaswamy et al., 2000), 4 moving average, 4 DGHL (Challu et al., 2022), 4 LSTMVAE (Park et al., 2018) and 4 RNN (Chang et al., 2017) models by varying hyperparameters for each model, for a total 19 combinations. Finally, to efficiently train our models, we sub-sampled all time-series with T > 2560 by a factor of 10. |