MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
Authors: Xinyao Fan, Yueying Wu, Chang Xu, Yuhao Huang, Weiqing Liu, Jiang Bian
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on real-world datasets demonstrate that our MG-TSD model outperforms existing time series prediction methods. In this section, we conduct extensive experiments on six real-world datasets to evaluate the performance of the proposed MG-TSD model and compare it with previous state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | University of British Columbia1, Peking University2, Nanjing University3, Microsoft Research4 |
| Pseudocode | Yes | Algorithm 1 Training procedure |
| Open Source Code | Yes | Our code is available at https://github.com/Hundredl/MG-TSD. |
| Open Datasets | Yes | (i) Solar: https://www.nrel.gov/grid/solar-power-data.html (ii) Electricity: https://archive.ics.uci.edu/dataset/321/electricit yloaddiagrams20112014 (iii) Traffic: https://archive.ics.uci.edu/dataset/204/pems+sf (iv) Taxi: https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.pa ge (v) KDD-cup: https://www.kdd.org/kdd2018/kdd-cup (vi) Wikipedia: https://github.com/mbohlkeschneider/gluon-ts/tree/mv _release/datasets |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, sample counts, or clear methodology for partitioning the data into these sets) for reproducibility. |
| Hardware Specification | Yes | All models are trained and tested on a single NVIDIA A100 80GB GPU. These experiments were executed using a single A6000 card with 48G memory capacity. |
| Software Dependencies | No | The MG-TSD code in this study is implemented using Py Torch (Paszke et al., 2019). It utilizes the Pytorch TS library (Rasul, 2021), which enables convenient integration of Py Torch models with the Gluon TS library (Alexandrov et al., 2020b) on which we heavily rely for data preprocessing, model training, and evaluation in our experiments. The paper mentions software by name but does not provide specific version numbers (e.g., 'PyTorch 1.9'). |
| Experiment Setup | Yes | We train our model for 30 epochs using the Adam optimizer with a fixed learning rate of 10-5. We set the mini-batch size to 128 for solar and 32 for other datasets. The diffusion step is configured as 100. Additional hyper-parameters, such as share ratios, granularity levels, and loss weights, are detailed in Appendix C.3. And Table 7 provides 'Tested hyper-parameter values for the MG-TSD Model.' |