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..
PrimeNet: Pre-training for Irregular Multivariate Time Series
Authors: Ranak Roy Chowdhury, Jiacheng Li, Xiyuan Zhang, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results show that Prime Net significantly outperforms state-of-the-art methods on naturally irregular and asynchronous data from Healthcare and Io T applications for several downstream tasks, including classification, interpolation, and regression. Experiment results show that Prime Net significantly outperforms all baselines on all datasets for all downstream tasks, under both few-shot and full training data settings. |
| Researcher Affiliation | Collaboration | Ranak Roy Chowdhury1, Jiacheng Li1, Xiyuan Zhang1, Dezhi Hong2, Rajesh K. Gupta1, Jingbo Shang1 1 University of California, San Diego 2 Amazon EMAIL, {xiyuanzh}@ucsd.edu, EMAIL |
| Pseudocode | Yes | Algorithm 1: Time CL Data Augmentation; Algorithm 2: Time Reco Data Augmentation |
| Open Source Code | Yes | Reproducibility Code is publicly available at https://github.com/ranakroychowdhury/Prime Net |
| Open Datasets | Yes | Datasets Physio Net Challenge 2012 (Silva et al. 2012) and MIMIC-III (Johnson et al. 2016) are multivariate time series datasets... Activity (Kaluˇza et al. 2010) dataset has 3-D positions... Appliances Energy (Tan et al. 2021) dataset contains... |
| Dataset Splits | No | During pretraining, we measure contrastive learning classification (i.e. how many samples are predicted correctly among the 2B sub-samples) and use the validation accuracy for early stopping. While validation is mentioned, specific details about dataset splits (e.g., percentages or exact counts for training, validation, and testing) are not provided. |
| Hardware Specification | No | The paper mentions 'efficient GPU implementation' but does not specify any particular GPU models, CPU models, or other hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions like 'PyTorch 1.9' or 'Python 3.8'). |
| Experiment Setup | Yes | We compute Cross-Entropy Loss for classification and Root Mean Squared Error (RMSE) for regression and interpolation. We conduct grid search on hyper-parameters, η = (0.3, 0.4, 0.5, 0.6, 0.7), α = (0.15, 0.05, 0.03), J = (1, 3, 5), µl, λl = (0.3, 0.4) and µu, λu = (0.7, 0.6) to report test results based on the best held-out validation performance. Best values for η = 0.5, 0.6, 0.5, 0.5 for Physio Net, MIMIC-III, Activity, Appliances Energy, respectively. |