Feature Learning for High-dimensional Functional Time Series

高维函数时间序列的特征学习

• 批准号: DP230102250
• 负责人: A/Prof Yanrong Yang
• 金额: $ 24.85万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230102250
• 关键词: Feature; Learning; dimensional; Functional; Time

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.

本计画旨在发展新的方法与理论，以研究在实证应用中所观察到的高维函数时间序列的共同特徴。其意义包括解决自适应和有效特征学习的关键差距，提高预测准确性，并全面了解经验数据的预测驱动因素。预期成果包括大数据理论的进步和应用研究人员易于实现的算法。该项目不仅有利于先进的制造业，通过寻找最佳的停止时间，木板压缩，但也上级预测死亡率在人口统计学，气候数据在环境科学，资产回报在金融，电力消耗在经济学。