Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics

08/17/2022
by   Zhengyang Zhou, et al.
USTC
NetEase, Inc
0

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.

READ FULL TEXT

page 1

page 4

02/09/2021

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

The high dynamics and heterogeneous interactions in the complicated urba...
05/25/2021

Quantifying Uncertainty in Deep Spatiotemporal Forecasting

Deep learning is gaining increasing popularity for spatiotemporal foreca...
08/21/2018

Machine Learning for Spatiotemporal Sequence Forecasting: A Survey

Spatiotemporal systems are common in the real-world. Forecasting the mul...
06/02/2022

Spatiotemporal models for Poisson areal data with an application to the AIDS epidemic in Rio de Janeiro

We present a class of spatiotemporal models for Poisson areal data suita...
02/28/2022

Disentangled Spatiotemporal Graph Generative Models

Spatiotemporal graph represents a crucial data structure where the nodes...
05/07/2020

Learning on dynamic statistical manifolds

Hyperbolic balance laws with uncertain (random) parameters and inputs ar...

Please sign up or login with your details

Forgot password? Click here to reset