Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction
Authors: Wei Ye, Prashant Khanduri, Jiangweizhi Peng, Feng Tian, Jun Gao, Jie Ding, Zhi-Li Zhang, Mingyi Hong
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of the proposed algorithm on real-world datasets with a number of time-series forecasting baselines implemented in both centralized and distributed manners. [...] Datasets. Our evaluation is conducted on a number of diverse real-world datasets: [D1] Power demand dataset (POWR) (EIA, 2022) [...] Evaluation metrics. For the multivariate time-series forecasting task, we utilize root mean square error (RMSE), which gauges the disparity within Euclidean space. For the multimodal time-series prediction with the Deep Sense 6G dataset, we use the distance-based accuracy (DBA) score; see the appendix for a detailed illustration. |
| Researcher Affiliation | Collaboration | University of Minnesota Twin Cities; Wayne State University; Meta Platforms, Inc. |
| Pseudocode | Yes | Algorithm 1 DIVIDE with label sharing [...] Algorithm 2 DIVIDE without label sharing |
| Open Source Code | No | Softwares assets. Our codes mainly rely on the following third-party libraries and all of them can be easily installed: Numpy1, scikit-learn2, Pytorch3, Py G4, multi-prophet5, DTAIDistance6, Pandas7, Matplotlib8. |
| Open Datasets | Yes | Datasets. Our evaluation is conducted on a number of diverse real-world datasets: [D1] Power demand dataset (POWR) (EIA, 2022), which contains the hourly power demand of thirteen major electricity grid service regions in the United States for 2022; [D2] NYC subway traffic dataset (SUBW) (EDDEN, 2021), which collects the number of people entering and exiting each subway station per hour in NYC, 2017 2021; [D3] Meteorological dataset (WEAT) (BENIAGUEV, 2017), which reports weather data from 36 regions with different indicators from Oct. 2012 to Nov. 2017. [D4] Deep Sense6G (DS6G) (Charan et al., 2022), which is a multimodal dataset containing sensory (e.g., camera, Radar, LiDAR) and radio information. Same with the ITU 2022 challenge (ITU, 2022), we use four scenarios in experiments. |
| Dataset Splits | Yes | Hyperparameters Settings. For all the experiments of neural network(NN)-based models, we split the datasets into the training, validation, and test sets with the ratio of 0.5 : 0.2 : 0.3. |
| Hardware Specification | Yes | Hardwares. Our workstation runs on the Unbuntu 18 system and is equipped with AMD Ryzen Threadripper PRO 3995WX CPU, 1TB memory, and three Nvidia RTX A6000 GPUs (48GB memory each). |
| Software Dependencies | No | Softwares assets. Our codes mainly rely on the following third-party libraries and all of them can be easily installed: Numpy1, scikit-learn2, Pytorch3, Py G4, multi-prophet5, DTAIDistance6, Pandas7, Matplotlib8. |
| Experiment Setup | Yes | Hyperparameters Settings. For all the experiments of neural network(NN)-based models, we split the datasets into the training, validation, and test sets with the ratio of 0.5 : 0.2 : 0.3. The min-max scaler is applied for the data normalization. All models are trained using the Adam (Kingma & Ba, 2014) optimizer with a batch size of 128. If there is no additional description, we follow the baseline model s default settings but set 64 hidden cells for all positions. Lastly, we ensure that the training is long enough so that the models converge well; we repeat each of the experiments three times, and report the average results. |