BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
Authors: Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our method on synthetic and real-world datasets, and the results show that Bay OTIDE outperforms the state-of-the-art methods in terms of accuracy and efficiency. |
| Researcher Affiliation | Collaboration | 1University of Utah, USA 2DAMO Academy, Alibaba Group 3Carnegie Mellon University, USA. |
| Pseudocode | Yes | Algorithm 1 Bay OTIDE |
| Open Source Code | Yes | We release the code at https://github.com/ xuangu-fang/Bay OTIDE. |
| Open Datasets | Yes | Traffic-Guangzhou(Chen et al.): traffic speed records in Guangzhou with 214 channels and 500 timestamps. Solar-Power(https://www.nrel.gov/ grid/solar-power-data.html) : 137 channels and 52560 timestamps, which records the solar power generation of 137 PV plants. Uber-Move(https:// movement.uber.com/): 7489 channels and 744 timestamps, recording the average movement of Uber cars along with the road segments in London, Jan 2020. |
| Dataset Splits | No | The paper states, 'For each dataset, we randomly sample {70%, 50%} of the available data points as observations for model training, and the rest for evaluation.' It mentions training and evaluation, but does not explicitly define a separate validation set or its split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments, such as particular GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions 'implemented it by Pytorch' but does not specify a version number for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | Detailed information on hyperparameter settings is provided at Table 5 in the appendix. |