III: Medium: Collaborative Research: Principled Uncertainty Quantification in Deep Learning Models for Time Series Analysis

时间序列数据在现代科学和工程中无处不在。在医疗保健系统、Web、网络监控、自动驾驶汽车和物联网服务等各种应用中收集的数据量前所未有。虽然深度学习在时间序列预测分析方面取得了巨大的成功，但这类模型的一个关键瓶颈是它们对预测中的不确定性一无所知。其后果是，他们可能会在没有注意到的情况下做出错误的预测--这将导致错误的决策，这在生命攸关的应用中可能是灾难性的。该项目旨在解决这个问题，并将深度学习推向更值得信赖的时间序列分析。该项目将使有原则的深度学习模型能够实现不确定性感知和可靠的时间序列回归和分类，而不会牺牲其预测能力。该项目的研究成果将被纳入研究生课程，教程和研讨会，将多个利益相关者和领域科学家聚集在一起。该项目的技术目标分为三个方面。首先，该项目将开发桥接深度序列模型的新技术（例如，递归网络，变压器）与高斯过程来量化函数空间中的不确定性。其次，该项目将探索如何学习校准的深度序列模型，以及如何进一步解耦不同的不确定性来源，以了解模型的预测不确定性来自何处。第三，该项目将利用不确定性来提高时间序列预测系统的可靠性和效率。这些技术将享受深度神经网络的表示能力，用于对时间序列数据中的复杂时间依赖性进行建模，同时提供量化和利用不确定性以实现鲁棒性和性能的原则性方法。开发的新模型，算法和技术将部署在两个重要的应用程序的时间序列分析：1）公共卫生监测和预测，2）实时分析的移动的传感时间序列数据。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Time series data are ubiquitous in modern science and engineering. An unprecedented amount is being collected in diverse applications such as healthcare systems, the Web, cyber network monitoring, self-driving cars, and Internet-of-Things services. While deep learning has achieved enormous success in time series predictive analysis, a key bottleneck of such models is that they are ignorant about the uncertainties in their predictions. A consequence is that they can produce wildly wrong predictions without noticing---this will lead to misguided decisions, which can be catastrophic in life-critical applications. This project aims to remedy this issue and advance deep learning towards more trustworthy time series analysis. The project will enable principled deep learning models for uncertainty-aware and reliable time series regression and classification without sacrificing their predictive power. Research findings from the project will be incorporated into graduate-level classes, tutorials, and workshops to bring multiple stakeholders and domain scientists together.The technical aims of this project are divided into three thrusts. First, the project will develop novel techniques bridging deep sequential models (e.g., recurrent networks, transformers) with Gaussian processes to quantify uncertainty in the functional space. Second, the project will explore how to learn calibrated deep sequential models and how to further decouple different sources of uncertainties to understand where a model's predictive uncertainty comes from. Third, the project will harness uncertainty to improve the reliability and efficiency of time series predictive systems. These techniques will enjoy the representation power of deep neural networks for modeling complex temporal dependencies in time-series data, while providing principled methodologies for quantifying and leveraging uncertainty for robustness and performance. The developed new models, algorithms, and techniques will be deployed in two important applications for times series analysis: 1) public health monitoring and forecasting, and 2) real-time analysis for mobile sensing time series data. The developed tools will also be open-sourced for trustworthy time series analysis that can benefit many other applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.