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..
Neural Contextual Anomaly Detection for Time Series
Authors: Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, Jan Gasthaus
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically on standard benchmark datasets that our approach obtains a state-of-the-art performance in the supervised, semi-supervised, and unsupervised settings. |
| Researcher Affiliation | Industry | Chris U. Carmona , Franc ois-Xavier Aubet , Valentin Flunkert , Jan Gasthaus Amazon Research EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks are present in the paper. The steps for the method are described in prose. |
| Open Source Code | Yes | open-source code of NCAD is available 2); 2https://github.com/awslabs/gluon-ts/tree/master/src/gluonts/ nursery/ncad |
| Open Datasets | Yes | We benchmark our method to others on five datasets... For the multivariate setting, we use: Soil Moisture Active Passive satellite (SMAP) and Mars Science Laboratory rover (MSL), two datasets published by NASA [Hundman et al., 2018]; and Server Machine Dataset (SMD)... For the univariate setting, we use: YAHOO, a dataset by [Yahoo! Labs, 2015]... And KPI, univariate dataset released in the AIOPS data competition by [Tsinghua Netman Lab, 2018]. |
| Dataset Splits | Yes | For both, following [Ren et al., 2019], we use the last 50% of the time points of each of the time series as test set and split the rest in 30% training and 20% validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions) are listed in the paper. |
| Experiment Setup | Yes | Hyperparameters were chosen using the validation set for YAHOO and KPI, and a standard setting is inferred for the other datasets (see extended article for details1). |