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Smart city article details
| Title | Towards Learning In Grey Spatiotemporal Systems: A Prophet To Non-Consecutive Spatiotemporal Dynamics |
|---|---|
| ID_Doc | 58207 |
| Authors | Zhou Z.; Yang K.; Sun W.; Wang B.; Zhou M.; Zong Y.; Wang Y. |
| Year | 2023 |
| Published | 2023 SIAM International Conference on Data Mining, SDM 2023 |
| DOI | http://dx.doi.org/10.1137/1.9781611977653.ch22 |
| Abstract | Spatiotemporal forecasting is an imperative topic in data science due to its critical applications in smart cities. Existing works mostly perform consecutive predictions of following steps with observations continuously obtained, where nearest observations can be exploited as the key knowledge for status estimation. However, the practical issues of early activity planning and sensor failures elicit a new task, non-consecutive forecasting. In this paper, we define spatiotemporal learning systems with missing observations as Grey Spatiotemporal Systems (G2S) and propose a Factor-Decoupled learning framework for G2S to hierarchically decouple multi-level factors, and enable flexible aggregations with uncertainty estimations. We especially select representative sequences to capture periodicity and instantaneous variations, and infer the non-consecutive future statuses under expected exogenous factors, compensating the missing observations. Given the inherent incompleteness and critical applications of G2S, a DisEntangled Uncertainty Quantification is put forward, to identify two types of uncertainty for model interpretations and robustness promotions. Experiments demonstrate that our solution can promote the performance by at least 8.50% on early planning and 2.01%-18.00% on sensor failures. The appendix of this paper can be found at https://github.com/zzyy0929/SDM-G2S. Copyright © 2023 by SIAM. |
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