Summary of Heterogeneity-informed Meta-parameter Learning For Spatiotemporal Time Series Forecasting, by Zheng Dong et al.
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
by Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, Xuan Song
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Heterogeneity-Informed Meta-Parameter Learning scheme aims to fully capture and leverage spatiotemporal heterogeneity in spatiotemporal time series forecasting. The approach learns spatial and temporal embeddings, which can be viewed as a clustering process, and then uses these embeddings to learn spatiotemporal-specific parameters from meta-parameter pools informed by the captured heterogeneity. This leads to the development of the Heterogeneity-Informed Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series forecasting. HimNet achieves state-of-the-art performance on five widely-used benchmarks while exhibiting superior interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spatiotemporal time series forecasting is important for many real-world applications. The challenge is to capture and use the differences in data between different locations and times. A new way of learning, called Heterogeneity-Informed Meta-Parameter Learning, is proposed. This approach learns about these differences by grouping similar data together and then uses this information to improve predictions. The method is tested on five different datasets and shows that it can make accurate predictions while being easy to understand. |
Keywords
* Artificial intelligence * Clustering * Spatiotemporal * Time series
