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..
Cross-Domain Contrastive Learning for Time Series Clustering
Authors: Furong Peng, Jiachen Luo, Xuan Lu, Sheng Wang, Feijiang Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and visualization analysis are conducted on 40 time series datasets from UCR, demonstrating the superior performance of the proposed model. |
| Researcher Affiliation | Academia | Furong Peng1, 2, Jiachen Luo1, 2, Xuan Lu3*, Sheng Wang4, Feijiang Li1, 2 1 Institute of Big Data Science and Industry, Shanxi University 2 School of Computer and Information Technology, Shanxi University 3 College of Physics and Electronic Engineering, Shanxi University 4 School of Automation, Zhengzhou University of Aeronautics |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 2https://github.com/Jiac Luo/CDCC |
| Open Datasets | Yes | experiments were conducted on 40 time series datasets from UCR1 (Dau et al. 2019). |
| Dataset Splits | No | The training and testing sets from the UCR were merged for evaluation. The paper mentions training and testing sets, but does not specify a separate validation split or explicit cross-validation methodology. |
| Hardware Specification | Yes | The experiments were conducted on a DCU Z100SM (16GB) computing card using Py Torch environment. |
| Software Dependencies | No | The experiments were conducted on a DCU Z100SM (16GB) computing card using Py Torch environment. While PyTorch is mentioned, a specific version number is not provided, nor are other software dependencies with version numbers. |
| Experiment Setup | Yes | In CDCC, τ I = 0.5, and τ C = 1. The learning rate, the number of layers in Bi LSTM, batch size and the dropout rate was searched. [...] We set α = 0.8, β = 1.1, and γ = 0.8. [...] In our experiments, we set ω = 0.1, θ = 0.1, and ϵ = 0.1. |