Feature Learning for High-dimensional Functional Time Series

该项目旨在开发新的方法和理论来研究高维函数时间序列在实际应用中观察到的共同特征。其意义包括解决自适应和有效的特征学习方面的关键差距，提高预测精度，以及全面理解经验数据的预测驱动因素。预期结果包括大数据理论的进步和应用研究人员易于实施的算法。该项目不仅通过找到木板压缩的最佳停止时间而使先进制造业受益，而且对人口统计学中的死亡率、环境科学中的气候数据、金融中的资产回报和经济中的电力消耗进行了更好的预测。

This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics.