Spatiotemporal forecasting is an imperative topic in data science due to its
diverse and critical applications in smart cities. Existing works mostly
perform consecutive predictions of following steps with observations completely
and continuously obtained, where nearest observations can be exploited as key
knowledge for instantaneous status estimation. However, the practical issues of
early activity planning and sensor failures elicit a brand-new task, i.e.,
non-consecutive forecasting. In this paper, we define spatiotemporal learning
systems with missing observation as Grey Spatiotemporal Systems (G2S) and
propose a Factor-Decoupled learning framework for G2S (FDG2S), where the core
idea is to hierarchically decouple multi-level factors and enable both flexible
aggregations and disentangled uncertainty estimations. Firstly, to compensate
for missing observations, a generic semantic-neighboring sequence sampling is
devised, which selects representative sequences to capture both periodical
regularity and instantaneous variations. Secondly, we turn the predictions of
non-consecutive statuses into inferring statuses under expected combined
exogenous factors. In particular, a factor-decoupled aggregation scheme is
proposed to decouple factor-induced predictive intensity and region-wise
proximity by two energy functions of conditional random field. To infer
region-wise proximity under flexible factor-wise combinations and enable
dynamic neighborhood aggregations, we further disentangle compounded influences
of exogenous factors on region-wise proximity and learn to aggregate them.
Given the inherent incompleteness and critical applications of G2S, a
DisEntangled Uncertainty Quantification is put forward, to identify two types
of uncertainty for reliability guarantees and model interpretations.

本文提出了一种基于因素解耦的灰色时空系统学习框架 FDG2S，用于处理存在缺失观测的非连续预测问题。该框架通过语义邻近序列采样和因子解耦聚合策略实现了对非连续状态预测的模拟。与此同时，该文中提出了一种解开不确定性的方法 DisEntangled Uncertainty Quantification 以保证模型的可靠性和可解释性。