Probabilistic Time Series Modeling with Decomposable Denoising Diffusion Model
Authors: Tijin Yan, Hengheng Gong, He Yongping, Yufeng Zhan, Yuanqing Xia
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 µ = 0, Σ = 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. |