Multi-Resolution Diffusion Models for Time Series Forecasting
Authors: Lifeng Shen, Weiyu Chen, James Kwok
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on nine real-world time series datasets demonstrate that mr-Diff outperforms state-of-the-art time series diffusion models. It is also better than or comparable across a wide variety of advanced time series prediction models. In this section, we conduct time series prediction experiments by comparing 22 recent strong prediction models on 9 popular real-world time series datasets. |
| Researcher Affiliation | Academia | Lifeng Shen1 , Weiyu Chen2, James T. Kwok2 1 Division of Emerging Interdisciplinary Areas, Hong Kong University of Science and Technology 2 Department of Computer Science and Engineering, Hong Kong University of Science and Technology |
| Pseudocode | Yes | The pseudocode for the training and inference procedures of the backward denoising process can be found in Appendix A. Algorithm 1 Training procedure of the backward denoising process. Algorithm 2 Inference procedure. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their proposed method. |
| Open Datasets | Yes | Experiments are performed on nine commonly-used real-world time series datasets (Zhou et al., 2021; Wu et al., 2021; Fan et al., 2022): (i) Nor Pool 1, (ii) Caiso 2, (iii) Traffic 3, (iv) Electricity 4, (v) Weather 5, (vi) Exchange 6 (Lai et al., 2018), (vii)-(viii) ETTh1 and ETTm1,7, (ix) Wind 8 (Li et al., 2022). |
| Dataset Splits | No | The paper mentions using a validation set to select history length but does not specify the exact percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments are run on an Nvidia RTX A6000 GPU with 48GB memory. |
| Software Dependencies | No | The paper mentions using Adam for training but does not provide specific version numbers for any software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | We train the proposed model using Adam with a learning rate of 10 3. The batch size is 64, and training with early stopping for a maximum of 100 epochs. K = 100 diffusion steps are used, with a linear variance schedule (Rasul et al., 2021) starting from β1 = 10 4 to βK = 10 1. S = 5 stages are used. The history length (in {96, 192, 336, 720, 1440}) is selected by using the validation set. |