Decoupled Invariant Attention Network for Multivariate Time-series Forecasting
Authors: Haihua Xu, Wei Fan, Kun Yi, Pengyang Wang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five datasets have demonstrated our superior performance with higher efficiency compared with state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Department of Computer and Information Science, University of Macau, China 2The State Key Laboratory of Internet of Things for Smart City, University of Macau, China 3University of Central Florida, USA 4Beijing Institute of Technology, China |
| Pseudocode | No | The paper describes the methodology using prose and mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Additional analysis on distribution shift in time series is provided in Appendix1. 1https://github.com/xhh39/DIAN |
| Open Datasets | Yes | We evaluate our proposed method on five real-world datasets and use the min-max normalization to normalize all these datasets. ... More detailed information about the datasets is provided in Appendix1. 1https://github.com/xhh39/DIAN |
| Dataset Splits | Yes | Except for the COVID-19 dataset, we split the datasets into training, validation, and test sets with the ratio of 7:2:1 in a chronological order. For the COVID-19 dataset, the ratio is 6:2:2 because of the limitation of data scale in temporal dimension. |
| Hardware Specification | Yes | All models were evaluated on a Linux server with one RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using 'Py Torch' for implementation but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | We use MAE (Mean Absolute Errors) as the loss function and the Adam Optimizer with a learning rate of 1e-3 with proper early stopping. For the main experiment, we fix the lookback window length as 12 and the horizon window length as 12. |