ANT: Adaptive Noise Schedule for Time Series Diffusion Models
Authors: Seunghan Lee, Kibok Lee, Taeyoung Park
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of our method across various tasks, including TS forecasting, refinement, and generation, on datasets from diverse domains. Code is available at this repository: https://github.com/seunghan96/ANT. |
| Researcher Affiliation | Academia | Seunghan Lee, Kibok Lee , Taeyoung Park Department of Statistics and Data Science, Yonsei University {seunghan9613,kibok,tpark}@yonsei.ac.kr |
| Pseudocode | Yes | Algorithm 1 Calculation of ANT score |
| Open Source Code | Yes | Code is available at this repository: https://github.com/seunghan96/ANT. |
| Open Datasets | Yes | In our experiments, we employ eight widely-used univariate datasets from various fields which can be found in Gluon TS [2] in their preprocessed form, with training and test splits provided. Table A.1 shows the statistics of the dataset, annotated with their corresponding frequencies (daily or hourly) and lengths of predictions. |
| Dataset Splits | Yes | Following TSDiff [15], the validation set is created by splitting a portion of the training dataset, with the split ratio determined by the sizes of the training and test datasets. |
| Hardware Specification | No | The paper discusses computational time and efficiency but does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Gluon TS' for data and metrics, but does not specify version numbers for any software dependencies like programming languages (e.g., Python), libraries (e.g., PyTorch, TensorFlow), or specific Gluon TS versions. |
| Experiment Setup | Yes | Table B.1 presents the hyperparameters of the backbone model utilized in our experiment, which are aligned with those used in TSDiff. Note that the diffusion step embeddings [35] are only applied to the model employing a non-linear schedule. Moreover, we employ skip connections for certain datasets, following the TSDiff approach, which results in improvements in validation performance. |