New Frontiers in Time Series Analysis

时间序列分析的新领域

• 批准号: 2114143
• 负责人: David Matteson
• 金额: $ 30万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114143&HistoricalAwards=false
• 关键词: New; Frontiers; Time; Series; Analysis

Big data are now prevalent in nearly every domain. While shrinkage and sparse estimators are essential to mitigate the volume issues posed by big datasets, innovative models are needed to address their variety and velocity demands. Indeed, enhanced monitoring and measurement systems now provide data at high enough resolutions that observations can often be considered intrinsically continuous or functional. Massive environmental, biological, industrial, and computational networks are being dynamically recorded such that their intricate evolution might be succinctly monitored, studied, and maintained. Vast datasets are generated every day, ranging from large-scale satellites and remote sensing instruments, emergency systems, and energy infrastructure, to nanoscale electromagnetic sensors, medical devices, and imaging devices. These systems provide rich information about our human-natural world, and most is sequential, or time-ordered and exhibit complex trends, transitions, and dependencies. Standard methods in multivariate statistics are unsuitable and insufficiently adaptable. The Principal Investigator (PI) will develop new methods and computational tools to aid data-driven scientific discovery and industry applications. The PI will also train and mentor graduate and undergraduate student research, freely disseminate new software and methodology across application areas, and foster collaboration between statistics and a wide range of fields, including economics, ecology, physics, finance, space weather, hydrology, agriculture, energy, environmental engineering, biophysics, mathematics, electrical engineering, human development, and computer science.Most time-indexed data exhibit heteroskedastic noise, anomalies, change points, local and global trends, and both linear and nonlinear dependence, and there is a striking shortage of analytical tools suitable for modeling such complexity. Shrinkage, sparse, and adaptive estimators are exceptions and have become vital tools. Global-local and regularized estimation, via adaptive sparsity/smoothness-inducing penalties/priors, is essential — it allows computationally tractable estimation of complex models, with greater interpretability and reduced estimation uncertainty. The PI will develop: (i) new methods and computational frameworks for change-point detection with increased flexibility by allowing global and segment-specific parameters; (ii) new theoretical and application-driven investigations into less explored aspects of hidden Markov models; (iii) extensions of dynamic shrinkage process for robust and adaptive estimation of change-points in the presence of outliers, spillover effects and causal inference on dependent network time series, and distributional trend filtering; (iv) new methods for simultaneous modeling and inference of dynamic functional data with complex features such as long range dependence and stochastic volatility.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.

大数据现在几乎在每个领域都很普遍。虽然收缩和稀疏估计器对于缓解大数据集带来的数量问题至关重要，但需要创新模型来满足其多样性和速度需求。事实上，增强的监测和测量系统现在提供足够高分辨率的数据，观测结果通常可以被认为本质上是连续的或功能性的。大规模的环境、生物、工业和计算网络正在被动态记录，以便可以简洁地监视、研究和维护它们复杂的演化。每天都会生成大量数据集，从大型卫星和遥感仪器、应急系统和能源基础设施，到纳米级电磁传感器、医疗设备和成像设备。这些系统提供了有关人类与自然世界的丰富信息，并且大多数是连续的或按时间排序的，并表现出复杂的趋势、转变和依赖性。多元统计中的标准方法不合适且适应性不足。首席研究员 (PI) 将开发新方法和计算工具，以帮助数据驱动的科学发现和行业应用。 PI还将培训和指导研究生和本科生的研究，在应用领域自由传播新的软件和方法，并促进统计学与广泛领域之间的合作，包括经济学、生态学、物理学、金融、空间天气、水文学、农业、能源、环境工程、生物物理学、数学、电气工程、人类发展和计算机科学。大多数时间索引数据表现出异方差 噪声、异常、变化点、局部和全局趋势以及线性和非线性依赖性，并且适合对此类复杂性进行建模的分析工具严重短缺。收缩、稀疏和自适应估计器是例外，并且已成为重要的工具。通过自适应稀疏性/平滑性诱导惩罚/先验进行全局-局部和正则化估计是至关重要的——它允许对复杂模型进行计算上易于处理的估计，具有更大的可解释性和减少的估计不确定性。 PI 将开发：（i）用于变化点检测的新方法和计算框架，通过允许全局和特定于段的参数来提高灵活性； (ii) 对隐马尔可夫模型较少探索的方面进行新的理论和应用驱动的研究； (iii) 动态收缩过程的扩展，用于在存在异常值、溢出效应和依赖网络时间序列的因果推断以及分布趋势过滤的情况下对变化点进行鲁棒和自适应估计； (iv) 对具有长期依赖性和随机波动性等复杂特征的动态函数数据进行同步建模和推理的新方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。