ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
Authors: Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, Jia Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through comprehensive experiments and comparisons with state-of-the-art time-series architectures and contemporary foundation models, we demonstrate the efficacy of Elas TST s unique design elements. |
| Researcher Affiliation | Collaboration | Jiawen Zhang DSA, HKUST(GZ) Guangzhou, China jiawe.zh@gmail.com Shun Zheng Microsoft Research Asia Beijing, China shun.zheng@microsoft.com Xumeng Wen Microsoft Research Asia Beijing, China xumengwen@microsoft.com Xiaofang Zhou CSE, HKUST Hong Kong SAR, China zxf@ust.hk Jiang Bian Microsoft Research Asia Beijing, China jiang.bian@microsoft.com Jia Li DSA, HKUST(GZ) Guangzhou, China jialee@ust.hk |
| Pseudocode | No | The paper includes an architectural overview diagram (Figure 1) but does not provide any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Elas TST is open-sourced at https://github.com/microsoft/Prob TS/tree/elastst. |
| Open Datasets | Yes | Our experiments leverage 8 well-recognized datasets, including 4 from the ETT series (ETTh1, ETTh2, ETTm1, ETTm2), and others include Electricity, Exchange, Traffic, and Weather. These datasets cover a wide array of real-world scenarios and are commonly used as benchmarks in the field. Detailed descriptions of each dataset are provided in Appendix C.1. Datasets are available at https://github.com/thuml/Autoformer under MIT License. |
| Dataset Splits | No | The paper mentions 'NMAE metric used for model checkpointing' implying the use of a validation set, but it does not explicitly provide the training/validation/test dataset splits (e.g., percentages or sample counts) within the paper. It refers to common practices in long-term forecasting [33]. |
| Hardware Specification | Yes | experiments are conducted on NVIDIA Tesla V100 GPUs with CUDA 12.1. |
| Software Dependencies | No | Elas TST is implemented using Py Torch Lightning [12]. The paper mentions this framework but does not provide specific version numbers for PyTorch Lightning or other key software libraries. |
| Experiment Setup | Yes | Elas TST is implemented using Py Torch Lightning [12], with a training regimen of 100 batches per epoch, a batch size of 32, and a total duration of 50 epochs. We use the Adam optimizer with a learning rate of 0.001... The range and specifics of these hyperparameters are documented in Appendix C.2. |