Diffusion-TS: Interpretable Diffusion for General Time Series Generation
Authors: Xinyu Yuan, Yan Qiao
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first study the interpretable outputs of the proposed model. Then we evaluate our method in two modes: unconditional and conditional generation, to verify the quality of the generated signals. [...] Finally, we conduct experiments to validate the performance of Diffusion TS under insufficient and irregular settings. Implementation details and ablation study can be found in Appendix G and C.7, respectively. |
| Researcher Affiliation | Academia | Xinyu Yuan, Yan Qiao Hefei University of Technology yxy5315@gmail.com, qiaoyan@hfut.edu.cn |
| Pseudocode | Yes | Algorithm 1 Reconstruction-guided Sampling and Algorithm 2 Optimized Conditional Sampling |
| Open Source Code | Yes | The code is available at https://github.com/Y-debug-sys/Diffusion-TS. |
| Open Datasets | Yes | We use 4 real-world datasets and 2 simulated datasets in Table 11 to evaluate our method. Stocks is the Google stock price data from 2004 to 2019. [...] ETTh dataset contains the data collected from electricity transformers, [...] Energy is a UCI appliance energy prediction dataset [...]. f MRI is a benchmark for causal discovery [...]. Sines has 5 features [...]. Mu Jo Co is multivariate physics simulation time series data [...]. Table 11: Dataset Details. Dataset # of Samples dim Link Sines 10000 5 https://github.com/jsyoon0823/TimeGAN Stocks 3773 6 https://finance.yahoo.com/quote/GOOG ETTh(1) 17420 7 https://github.com/zhouhaoyi/ETDataset Mu Jo Co 10000 14 https://github.com/deepmind/dm_control Energy 19711 28 https://archive.ics.uci.edu/ml/datasets f MRI 10000 50 https://www.fmrib.ox.ac.uk/datasets |
| Dataset Splits | No | We use 90% of the dataset for training and the rest for testing. |
| Hardware Specification | Yes | A single Nvidia 3090 GPU is used for model training. |
| Software Dependencies | No | The paper mentions software components like GRU-based neural networks and implicitly PyTorch (from the prompt, not paper text directly), but does not provide specific version numbers for these or other software dependencies required for reproduction. |
| Experiment Setup | Yes | We did limited hyperparameter tuning in this study to find default hyperparemters that perform well across datasets. The range considered for each hyper-parameter is: batch size : [32; 64; 128], the number of attention heads: [4; 8], the number of basic dimension: [32, 64, 96, 128], the diffusion steps: [50, 200, 500, 1000] and the guidance strength: [1., 1e-1, 5e-2, 1e-2, 1e-3]. [...] In all of our experiments, we use cosine noise scheduling and optimize our network using Adam with (β1, β2) = (0.9, 0.96). And a linearly decay learning rate starts at 0.0008 after 500 iterations of warmup. For conditional generation, we set the inference steps, γ to be 200, 0.05 respectively. Table 8: Hyperparameters, training details, and compute resources used for each model |