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..
Detecting Anomalous Event Sequences with Temporal Point Processes
Authors: Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that the proposed statistic excels at both traditional Go F testing, as well as at detecting anomalies in simulated and real-world data. |
| Researcher Affiliation | Collaboration | Oleksandr Shchur Technical University of Munich EMAIL Ali Caner Türkmen Amazon Research EMAIL Tim Januschowski Amazon Research EMAIL Jan Gasthaus Amazon Research EMAIL Stephan Günnemann Technical University of Munich EMAIL |
| Pseudocode | Yes | We provide the pseudocode description of our Oo D detection method in Appendix D. |
| Open Source Code | Yes | Code and datasets: https://github.com/shchur/tpp-anomaly-detection |
| Open Datasets | Yes | Code and datasets: https://github.com/shchur/tpp-anomaly-detection"; "LOGS: We generate server logs using Sock Shop microservices (Weave, 2017)"; "STEAD (Stanford Earthquake Dataset) (Mousavi et al., 2019) |
| Dataset Splits | Yes | We use the training set Dtrain to bit an RNN-based neural TPP model (Shchur et al., 2020) via maximum likelihood estimation (see Appendix F for details). Then, we de bone test statistics for the general TPP as follows."; "We split one large server log into 30-second subintervals, that are then partitioned into train and test sets. |
| Hardware Specification | Yes | The experiments were run on a machine with a 1080Ti GPU. |
| Software Dependencies | No | The paper mentions using an RNN-based neural TPP model and tools like Sock Shop microservices and Pumba, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Details on the setup and datasets construction are provided in Appendix E & F."; "We use the training set Dtrain to bit an RNN-based neural TPP model (Shchur et al., 2020) via maximum likelihood estimation (see Appendix F for details). |