III: Small: Robust Algorithms for Multi-Task Learning of Spatio-Temporal Data

近年来，从不同地理位置在不同时间点收集的数据（称为时空数据）呈爆炸式增长。时空数据是通过各种传感技术和科学模拟产生的。对大时空数据的分析至关重要，因为它支持国家重要的关键应用，如环境可持续性，医疗保健和国家安全。然而，为了实现其全部好处，必须克服主要的分析和计算挑战。这项跨学科的研究旨在通过开发创新的预测算法来克服一些挑战。作为概念证明，这些算法将被纳入一个由主要研究人员之一开发的集合预测系统，以提高国家的季节性干旱预测能力。将创建基准数据集，并提供给其他研究人员，以评估他们的算法。该项目网页（http：//www.cse.msu.edu/project/mtl）将用于传播实验结果、发表的论文和该项目开发的软件。参与该项目的研究生将接受培训，作为下一代数据科学家进行尖端的多学科研究。有许多时空预测任务涉及解决多个相关的子问题。与其独立地为每个子问题设计预测模型，还不如通过利用它们的时空自相关性来联合解决预测任务，从而使其自然适合应用多任务学习范式。该提案围绕以下四个关键贡献来解决将多任务学习应用于大规模时空数据的挑战。首先，将开发一种具有空间效率的在线多任务学习算法，该算法具有理论上证明的收敛速度，可以处理大量数据。其次，将开发一个深度学习框架，以提取用于多任务预测的显著时空特征。第三，将开发新的多任务学习算法来处理多尺度变量，随着高分辨率传感器和以越来越精细的分辨率生成的模拟数据的激增，多尺度变量变得越来越普遍。最后，所提出的算法将被应用到各种时空域，包括气候和环境科学。总体而言，拟议的研究将通过开发创新的预测算法来推进当前的最新技术水平，这些算法考虑了数据固有的空间和时间变化性，并提供具有高预测准确性和学习效率的解决方案。

Recent years have witnessed an explosive growth of data collected from different geographical locations at different time points, known as spatio-temporal data. Spatio-temporal data are generated from a wide array of sensing technology and through scientific simulations. Analysis of the big spatio-temporal data is crucial as it supports key applications of national importance such as environmental sustainability, healthcare, and national security. However, in order to realize its full benefits, there are major analytical and computational challenges that must be overcome. This interdisciplinary research seeks to overcome some of the challenges by developing innovative prediction algorithms. As proof of concept, the algorithms will be incorporated into an ensemble prediction system developed by one of the principal investigators to enhance the nation's seasonal drought forecast capability. Benchmark data sets will be created and made available to other researchers for evaluating their algorithms. The project web page (http://www.cse.msu.edu/~ptan/project/mtl) will be used to disseminate experimental results, published papers, and software developed in this project. The graduate students who participate in the project will be trained to conduct cutting edge multidisciplinary research as next-generation data scientists.There are many spatio-temporal prediction tasks that involve solving multiple related sub-problems. Rather than designing the prediction model for each sub-problem independently, it would be desirable to solve the prediction tasks jointly by exploiting their spatio-temporal autocorrelations, thus making it a natural fit for applying a multi-task learning paradigm. This proposal centers around the following four key contributions to address the challenges of applying multi-task learning to large-scale spatio-temporal data. First, a space-efficient online multi-task learning algorithm, with theoretically proven convergence rates, will be developed to handle the massive volume of data. Second, a deep learning framework will be developed to extract salient spatio-temporal features for multi-task predictions. Third, novel multi-task learning algorithms will be developed to deal with multi-scale variables, which are becoming increasingly prevalent with the proliferation of high-resolution sensors and simulation data generated at increasingly finer resolutions. Finally, the proposed algorithms will be applied to various spatio-temporal domains, including climate and environmental sciences. Overall the proposed research will advance current state-of-the-art by developing innovative prediction algorithms that consider the inherent spatial and temporal variability of the data and provide solutions with high predictive accuracy and learning efficiency.