CAREER: New Frontiers in Time Series Analysis

大数据渗透到商业、工程和科学领域--即使不包括手机、平板电脑和PC，联网智能设备的数量预计也将在五年内从数十亿增长到数百亿。传感器、GPS、RFID、医疗设备以及应急和能源系统产生了大量数据，提供了有关现代世界未知方面的丰富信息。尽管挖掘这些数据的普遍性和重大利益，有几个现有的分析工具是合适的。研究人员重点关注新统计方法的开发，将这些方法的应用扩展到新领域，以及提高对数据驱动模型构建和推理中理论挑战的理解。正在开发的方法有可能加强许多领域的研究，包括天文学，经济学，紧急医疗服务，神经科学和统计本身。时间序列分析是一个丰富的和历史悠久的领域，但它仍然集中在单变量和低维多变量分析。近年来，大数据已经开始渗透到商业、工程和科学领域，但尽管人们对挖掘这些数据普遍存在并产生了极大的兴趣，但现有的分析工具很少适合于具有时间结构格式的数据。研究人员研究新的高维函数时间序列（HDTS）工具的开发，以帮助研究人员和从业者满足日益雄心勃勃的推理和建模目标。具体来说，研究人员研究：（i）同时建模，推理和预测动态功能数据的新方法;（ii）建模高维时间有序数据的新结构正则化方法;（iii）大数据监控系统的新自适应稳定性分析方法;以及（iv）将这些新方法与新兴的调查路线联系起来，并为回答广泛领域的关键研究问题提供基础设施。

Big data permeates business, engineering, and science -- the number of connected smart devices, even excluding phones, tablets, and PCs, is projected to grow from billions to tens of billions within five years. Vast data is generated from sensors, GPS, RFID, medical devices, and emergency and energy systems, to provide rich information about untold aspects of the modern world. Despite the ubiquity and significant interest in mining such data, there are few existing analytical tools that are suitable. The investigator focuses on the development of new statistical methodology, extending application of these methods to new fields, and on increasing understanding of the theoretical challenges in data-driven model building and inference. The methods under development have the potential to strengthen research in numerous fields, including astronomy, economics, emergency medical services, neuroscience, and statistics itself.Time series analysis is a rich and historic field, but it remains centered on univariate and low dimensional multivariate analysis. In recent years, big data has begun to permeate business, engineering, and science, but despite the ubiquity and significant interest in mining such data, there are few existing analytical tools suitable for data with a time structured format. The investigator studies the development of new high dimensional and functional time series (HDTS) tools to help researchers and practitioners meet increasingly ambitious inferential and modeling aims. Specifically, the investigator studies: (i) new methods for simultaneous modeling, inference, and forecasting of dynamic functional data; (ii) new structured regularization methods for modeling high dimensional time ordered data; (iii) new adaptive, yet methods of stability analysis for big data monitoring systems; and (iv) linking these new methods with emergent lines of inquiry and providing an infrastructure for answering critical research questions in a wide range of fields.