DeepAI AI Chat
Log In Sign Up

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

by   Zhengyang Zhou, et al.

The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests. Given the fact that uncertainty quantifies the potential variations around prediction results, traditional learning schemes always lack uncertainty labels, and conventional uncertainty quantification approaches mostly rely upon statistical estimations with Bayesian Neural Networks or ensemble methods. However, they have never involved any spatiotemporal evolution of uncertainties under various contexts, and also have kept suffering from the poor efficiency of statistical uncertainty estimation while training models with multiple times. To provide high-quality uncertainty quantification for spatiotemporal forecasting, we propose an uncertainty learning mechanism to simultaneously estimate internal data quality and quantify external uncertainty regarding various contextual interactions. To address the issue of lacking labels of uncertainty, we propose a hierarchical data turbulence scheme where we can actively inject controllable uncertainty for guidance, and hence provide insights to both uncertainty quantification and weak supervised learning. Finally, we re-calibrate and boost the prediction performance by devising a gated-based bridge to adaptively leverage the learned uncertainty into predictions. Extensive experiments on three real-world spatiotemporal mobility sets have corroborated the superiority of our proposed model in terms of both forecasting and uncertainty quantification.


page 2

page 5

page 11


Quantifying Uncertainty in Deep Spatiotemporal Forecasting

Deep learning is gaining increasing popularity for spatiotemporal foreca...

Statistical downscaling with spatial misalignment: Application to wildland fire PM_2.5 concentration forecasting

Fine particulate matter, PM_2.5, has been documented to have adverse hea...

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

Spatiotemporal forecasting is an imperative topic in data science due to...

Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning

Predictions made by deep learning models are prone to data perturbations...

Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey

Machine learning methods are increasingly widely used in high-risk setti...

Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification

Deep learning models achieve state-of-the art results in predicting bloo...