Summary of Pdetime: Rethinking Long-term Multivariate Time Series Forecasting From the Perspective Of Partial Differential Equations, by Shiyi Qi et al.
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
by Shiyi Qi, Zenglin Xu, Yiduo Li, Liangjian Wen, Qingsong Wen, Qifan Wang, Yuan Qi
First submitted to arxiv on: 25 Feb 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 paper presents a novel approach to long-term multivariate time-series forecasting (LMTF) called PDETime. Inspired by Neural PDE solvers, PDETime follows encoding-integration-decoding operations and is designed to adapt to the intrinsic spatiotemporal nature of the data. The model achieves state-of-the-art results on seven diverse real-world LMTF datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new approach to forecasting time series looks at the data as “spatiotemporal” – a combination of space and time. This is different from other models that only look at past values or just the timing of events. The PDETime model uses this idea to make predictions about future time series. It’s tested on lots of real-world data and does better than other models. |
Keywords
* Artificial intelligence * Spatiotemporal * Time series
