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..
Probabilistic Time Series Modeling with Decomposable Denoising Diffusion Model
Authors: Tijin Yan, Hengheng Gong, He Yongping, Yufeng Zhan, Yuanqing Xia
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on 8 real-world datasets show that D3M reduces RMSE and CRPS by up to 4.6% and 4.3% compared with state-of-the-arts on imputation tasks, and achieves comparable results with state-of-the-arts on forecasting tasks with only 10 steps. |
| Researcher Affiliation | Academia | 1School of Automation, Beijing Institute of Technology, Beijing, China. 2Zhongyuan University of Technology, Zhengzhou, Henan, China. |
| Pseudocode | Yes | Algorithm 1 Unconditional training procedure of D3M. |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating the release of open-source code for the methodology described. |
| Open Datasets | Yes | For time series imputation tasks, we use the Physio Net Challenge 2012 and Air quality for evaluation. Detailed description of these datasets can be found in Appendix B.1. [...] We use six real-world datasets for evaluation: Exchange, Solar, Electricity, Traffic, Taxi and Wikipedia. All of these datasets can be obtained from Gluon TS (Alexandrov et al., 2020). |
| Dataset Splits | No | The paper mentions masking proportions for testing but does not explicitly provide train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Gluon TS' but does not provide specific version numbers for it or any other software dependencies needed to replicate the experiments. |
| Experiment Setup | Yes | The number of steps for inference is set as 10 for all experiments. We set ยต = 0, ฮฃ = I for simplicity. For D3M with Linear type for h(t), we set a = X0, b = X0 / 2. We use the grid-search method for the hyper-parameters of EMA and linear gated attention module. Specifically, we set candidates of h as {8, 12, 16}, candidates of z as {32, 64, 96}, candidates of v as {128, 160, 256}. The batch size and epochs are set as 16 and 300, separately. In addition, we use a multi-step learning rate scheduler which decays the learning rate at 75% and 90% of all epochs. |