. . A systematic study of uncertainty quantication methods for spatiotemporal forecasting has been missing in the community. An official website of the United States government. However, the . Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. Deep learning is gaining increasing popularity for spatiotemporal forecasting. The spatiotemporal forecasting (STF) problem refers to the forecasting of the unknown system states in time and space. Proposed model shows the best performance compared with other deep learning models. Quantifying Uncertainty in Deep Spatiotemporal Forecasting - GitHub - DongxiaW/Quantifying_Uncertainty_in_Deep_Spatiotemporal_Forecasting: Quantifying Uncertainty in Deep Spatiotemporal Forecasting However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions . This work develops a scalable deep ensemble approach to quantify uncertainties for DCRNN, a state-of-the-art method for short-term trafc forecasting and shows that it outperforms the current state of theart Bayesian and number of other commonly used frequentist techniques. An official website of the United States government. Spearheaded the uncertainty quantification study for spatiotemporal forecasting, compared Bayesian and Frequentist methods on traffic and COVID-19 predictions. Ubicomp 2019, Combining Physical and Data-Driven Knowledge in Ubiquitous Computing Workshop. 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. Quantifying Forecast Uncertainty John W. Dennis Evan Miyakawa James Bishop Alan B. Gelder. Vienna, Austria & Online | 23-27 May 2022. Spatiotemporal information is used for solar irradiation prediction for a region. Quantifying uncertainty is critical to risk assessment and decision making in high stakes domains. Quantifying Uncertainty in Deep Spatiotemporal Forecasting; Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting; ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting; TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction; ELITE : Robust Deep Anomaly Detection with Meta Gradient However, prior works for deep neural network uncertainty estimation have mostly focused on point prediction. Rotation-Equivariant Convolutional Neural Network Ensembles. However, prior works for deep neural network uncertainty estima-tion have mostly focused on point prediction. Finally, we re-calibrate and boost the prediction performance by devising a gated-based bridge to adaptively leverage the learned uncertainty into predictions. This work develops a scalable deep ensemble approach to quantify uncertainties for DCRNN, a state-of-the-art method for short-term trafc forecasting and shows that it outperforms the current state of theart Bayesian and number of other commonly used frequentist techniques. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. Quantifying Uncertainty in Deep Spatiotemporal Forecasting Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu Deep learning is gaining increasing popularity for spatiotemporal forecasting. In this letter we aim to investigate the problem of cellular traffic prediction over a metropolitan area and propose a deep regression (DR) approach to model its complex spatio-temporal dynamics. In. Deep-learning-based data-driven forecasting methods have produced impressive results for trafc forecasting. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. In this paper, we conduct a systematic study of deep uncertainty quantication for spatiotemporal forecasting. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. Title: Uncertainty Quantification in Deep Spatiotemporal Forecasting and Decision Making . Deep learning is gaining increasing popularity for spatiotemporal forecasting. Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression . Here's how you know In this study, we combined a land-use change dataset with a satellite-based high-resolution biomass and soil organic carbon dataset to . Since the scales are different, the solid blue line indicates where NN and RIO/SVGP have same prediction RMSE. Deep learning is gaining increasing popularity for spatiotemporal forecasting. multivariate time series [10]. Hence, a systematic study of uncertainty . April 2021: Featured speaker at NVIDIA GTC 2021! Thus, a dot below the line means that the . Many works have used deep learning for spatiotemporal forecasting, applied to various domains from weather [60, 61] to traffic [20, 33] forecasting. In this paper, we analyze forecasting uncertainty in . 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. A systematic study of uncertainty quantification methods for spatiotemporal forecasting has been missing in the community. Quantifying Uncertainty in Deep Spatiotemporal Forecasting for COVID-19. I will discuss (1) a systematic study of UQ for deep spatiotemporal forecasting. We investigate the evaluation metrics, properties of both Frequentist and Bayesian UQ methods, as well as their practical performances. Abstract: Deep learning is gaining increasing popularity for spatiotemporal forecasting.However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. About this Publication The work was conducted by the Institute for Defense Analyses (IDA) under CRP C6614. Quantifying Uncertainty in Deep Spatiotemporal Forecasting for COVID-19. STF is widely seen and used in numerous real-world applications (Bauer et al., 2015; Ham et al., 2019; Sharma and Kakkar, 2018; Shi et al., 2015).In numerical weather prediction (Bauer et al., 2015), the forecasting of rainfall intensity over a region for the . Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption . 3 am, 9 am, 12 pm, and 11 pm. Deep-learning-based data-driven forecasting methods have produced impressive results for trafc forecasting. link Reviewer: Time Series Workshop @ ICML 2021. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. . Land-use change is supposed to exert significant effects on the spatio-temporal patterns of ecosystem carbon storage in arid regions, while the relative size of land-use change effect under future environmental change conditions is still less quantified. @article{wu2021quantifying, title={Quantifying Uncertainty in Deep Spatiotemporal Forecasting}, author={Wu, Dongxia and Gao, Liyao and Xiong, Xinyue and Chinazzi, Matteo and Vespignani, Alessandro and Ma, Yi-An and Yu, Rose}, journal={Proceedings of the 29th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD)}, year={2021} } In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. Rotation-Equivariant Convolutional Neural Network Ensembles. Introduction. For More Information: Dr. Alan B. Gelder, Project Leader agelder@ida.org, 703-845-6879 However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. An advanced deep learning model is used to handle spatiotemporal data. Spearheaded the uncertainty quantification study for spatiotemporal forecasting, compared Bayesian and Frequentist methods on traffic and COVID-19 predictions. Mar 2021: L4DC 2021: Learning Dynamical Systems and . 1. Deep learning is gaining increasing popularity for spatiotemporal forecasting. We provide a recipe for practitioners when facing UQ problems in deep spatiotemporal forecasting. Deep learning is gaining increasing popularity for spatiotemporal forecasting. We also plot the aleatoric uncertainty on top of . Variational inference is employed in the model to quantify forecast uncertainty. In this paper, we conduct a benchmark study of deep uncertainty quantification for spatiotemporal forecasting. NeurIPS, COVID-19 Symposium. (a) COV between 0.2 to 0.3 (b) COV between 0.3 to 0.4 (c) COV between 0.4 to 0.5 (d) COV above 0.5 Figure 8: 60-min-ahead traffic forecasting on four sensors selected from 4 COV bins. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. Figure 3: Comparison among NN, RIO, and SVGP. Quantifying Uncertainty in Deep Spatiotemporal Forecasting; Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yian Ma, Rose Yu. STF is widely seen and used in numerous real-world applications (Bauer et al., 2015; Ham et al., 2019; Sharma and Kakkar, 2018; Shi et al., 2015).In numerical weather prediction (Bauer et al., 2015), the forecasting of rainfall intensity over a region for the . We analyze UQ methods from both the Bayesian and the frequentist point of view, casting in a unified framework. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. outcomes from the first IAVCEI-WMO workshop on Ash Dispersal Forecast and Civil . Quantifying uncertainty is critical to risk assessment and decision making in high stakes domains. link Reviewer: Time Series Workshop @ ICML 2021. Quantifying Uncertainty in Deep Spatiotemporal Forecasting Dongxia Wu UC San Diego La Jolla, CA, USA dowu@ucsd.edu Liyao Gao University of Washington . . link. A major . Quantifying Uncertainty in Deep Spatiotemporal Forecasting; Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yian Ma, Rose Yu. Ubicomp 2019, Combining Physical and Data-Driven Knowledge in Ubiquitous Computing Workshop. . The horizontal axis denotes the prediction RMSE of the NN, and the vertical axis the prediction RMSE of RIO (blue dots) and SVGP (yellow dots). Abstract: Quantifying uncertainty is critical to risk assessment and decision making in high stakes domains. our approach uses a scalable bayesian optimization method to perform hyperparameter optimization, selects a set of high-performing configurations, fits a generative model to capture the joint distributions of the hyperparameter configurations, and trains an ensemble of models by sampling a new set of hyperparameter configurations from the The aleatoric uncertainty or 95% prediction interval is shown in yellow shade, and the epistemic uncertainty is shown in blue shade. Introduction. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. DR is instrumental in capturing multi-scale and multi-domain . In this talk, I will present our efforts in uncertainty quantification (UQ) in learning spatiotemporal dynamics. Quantifying Uncertainty in Deep Spatiotemporal Forecasting Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021 [ Paper ] [ Code ] Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems NeurIPS, COVID-19 Symposium. Machine Learning for Mobile Health. 1. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. Quantifying Uncertainty in Deep Spatiotemporal Forecasting D Wu, L Gao, X Xiong, M Chinazzi, A Vespignani, YA Ma, R Yu 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 1841-1851 , 2021 Machine Learning for Mobile Health. The posterior ensemble spread represents uncertainty in the inversion estimate of the ash emissions. Quantifying Uncertainty in Deep Spatiotemporal Forecasting Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. Here's how you know The spatiotemporal forecasting (STF) problem refers to the forecasting of the unknown system states in time and space. In high-stakes domains, being able to generate probabilistic forecasts with confidence intervals is . Quantifying Uncertainty in Deep Spatiotemporal Forecasting - GitHub - DongxiaW/Quantifying_Uncertainty_in_Deep_Spatiotemporal_Forecasting: Quantifying Uncertainty in Deep Spatiotemporal Forecasting A systematic study of uncertainty quantification methods for spatiotemporal forecasting has been missing in the community. However, prior works for deep neural network uncertainty estimation have mostly focused on point prediction. May 2021: KDD 2021: Uncertainty Quantification in Deep Spatiotemporal Forecasting! Deep learning is gaining increasing popularity for spatiotemporal forecasting. link. We investigate spa-tiotemporal forecasting problems on a regular grid as well as on a A major . However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. About EGU22 FAQs Finally, we re-calibrate and boost the prediction performance by devising a gated-based bridge to adaptively leverage the learned uncertainty into predictions. Each dot represents an independent experimental run. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making.