CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement
Authors: Qihe Huang, Lei Shen, Ruixin Zhang, Shouhong Ding, Binwu Wang, Zhengyang Zhou, Yang Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on 8 real-world MTS datasets demonstrate the effectiveness of Cross GNN compared with state-of-the-art methods. |
| Researcher Affiliation | Collaboration | University of Science and Technology of China (USTC), Hefei, China Suzhou Institute for Advanced Research, USTC, Suzhou, China Youtu Laboratory, Tencent, Shanghai, China |
| Pseudocode | No | The paper describes the methodology using mathematical equations and textual explanations, but does not include pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/hqh0728/Cross GNN. |
| Open Datasets | Yes | We conduct extensive experiments on 8 real-world datasets following [27], including Weather, Traffic, Exchange Rate, Electricty and 4 ETT datasets(ETTh1, ETTh2, ETTm1 and ETTm2). Table 4: The statistics of the datasets for MTS forecasting. Datasets ETTh1 ETTh2 ETTm1 ETTm2 Weather Traffic Exchange-rate Electricity |
| Dataset Splits | Yes | We follow the standard protocol in [27] and split datasets into training, validation and test set by the ratio of 6:2:2 for the last 4 ETT datasets, and 7:1:2 for the other datasets. |
| Hardware Specification | Yes | The model is implemented in Py Torch 1.8.0 and trained on a single NVIDIA Tesla V100 PCIe GPU with 16GB memory. Comparison experiments are implemented on a Intel(R) 8255C CPU @ 2.50GHZ with 40GB memory, centos 7.8, and TVM 1.0.0. |
| Software Dependencies | Yes | The model is implemented in Py Torch 1.8.0 and trained on a single NVIDIA Tesla V100 PCIe GPU with 16GB memory. Comparison experiments are implemented on a Intel(R) 8255C CPU @ 2.50GHZ with 40GB memory, centos 7.8, and TVM 1.0.0. |
| Experiment Setup | Yes | All the models are following the same experimental setup with prediction length T {96, 192, 336, 720} for all datasets as in the original papers. We set the scale numbers S to 5 and set K to 10 for all datasets, as sensitivity experiments have shown that S does not have a significant impact beyond 5 and Cross GNN is not sensitive to K. Additionally, the mean squared error (MSE) is used as the loss function. For the learning rate, a grid search is conducted among [5e-3, 1e-3, 5e-4, 1e-4, 5e-5, 1e-5] to obtain the most suitable learning rate for all datasets. Besides, the dimension of the channel is set as 8 for smaller datasets and 16 for larger datasets, respectively. The training would be terminated early if the validation loss does not decrease for three consecutive rounds. |