CAREER: A Novel Framework for Knowledge Discovery from Time Series Data in Biology and Climate Science

传感器和高吞吐量数据采集技术的最新进展使得收集大量数据，尤其是多个领域中的时间序列数据（例如，气候科学、生物科学）。虽然已经开发了各种各样的技术来聚类和挖掘这些数据，但从时间序列数据中提取因果关系的可扩展算法的进展有限。该项目旨在开发基于格兰杰因果关系的新型机器学习模型，以揭示高维时间序列的复杂依赖结构。由此产生的算法将在两个现实世界的应用程序的背景下进行评估该项目旨在解决从时间序列数据进行数据分析的三个基本挑战，包括：（1）从时间序列数据中发展因果关系分析的理论基础，以量化格兰杰因果关系与真实因果关系之间的差距，（2）发展一个统一的架构，把不同类型的领域知识纳入数据分析，以及（3）研究有效的解决方案，以解决重要但通常被忽视的实际问题，包括时间序列的不规则性和可扩展性。将在两个真实的应用上评估所得到的算法，即，通过与生物学和气候科学的研究人员合作，在免疫系统和气候变化归因中发现基因调控网络。拟议的研究可能会影响从高维时间序列数据中发现因果关系的多个应用领域。该项目预计将推进时间序列数据的数据分析技术的理论基础，并提供一个统一的框架，可以很容易地集成领域知识。 这个项目的结果可以预期显着推进当前的艺术状态，从时间序列数据中引出有关因果关系的见解。除了核心研究进展外，该项目还提供基于工作流程的易于使用的软件，用于向具有广泛背景的学生，研究人员和从业人员教授机器学习。教育和推广活动包括新的跨学科课程，研讨会，辅导和高中访问。这项工作产生的软件和数据将免费传播给更广泛的研究和教育界。有关该项目的更多信息，请访问：http://www-bcf.usc.edu/~liu32/uscTimeSeries.htm。

Recent advances in sensors and high throughput data acquisition technologies have made it possible to collect massive amount of data, and especially time series data in a number of domains (e.g., climate sciences, biological sciences). While a wide range of techniques have been developed for clustering and mining such data, there has been limited progress on scalable algorithms for extracting causal relationships from time series data. This project aims to develop novel machine learning models based on Granger causality to uncover the complex dependence structures from high-dimensional time series. The resulting algorithms will be evaluated in the context of two real-world applications (climate change, computational biology).The project aims to address three fundamental challenges of data analysis from time series data, including: (1) developing the theoretical foundations of causality analysis from time series data to quantify the gap between Granger causality and true causality, (2) developing a unified framework to incorporate different types of domain knowledge in data analysis, and (3) examining effective solutions to important but usually overlooked practical issues, including irregular nature of the time series and scalability. The resulting algorithms will be evaluated on two real applications, i.e., gene regulatory network discovery in immune systems and climate change attribution, by collaborating with researchers in biology and climate science.The proposed research could impact multiple application domains where discovery of causal relationships from high dimensional time series data is of interest. The project is expected to advance the theoretical foundations of data analytic techniques for time-series data and provide a unified framework that can easily integrate domain knowledge. The results of this project can be expected to significantly advance the current state of the art in eliciting insights regarding causal relationships from time series data. In addition to the core research advances, this project contributes easy-to-use software based on workflows for teaching machine learning to students, researchers and practitioners with a broad range of backgrounds. Educational and outreach activities include new interdisciplinary courses, workshops, tutorials, and high-school visits. Software and data resulting from this work will be freely disseminated to the broader research and educational community. Additional information about the project can be found at: http://www-bcf.usc.edu/~liu32/uscTimeSeries.htm.