Efficient and effective methods for classifying massive time series data

海量时间序列数据高效有效的分类方法

• 批准号: DP240100048
• 负责人: Prof Geoffrey Webb
• 金额: $ 35.97万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240100048
• 关键词: Efficient; effective; methods; classifying; massive

This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given computational budget. Expected outcomes of this project include efficient, effective and broadly applicable time series classification technologies. This should provide significant benefits to myriad sectors, transforming data science for time series problems and supporting innovation in industry, commerce and government.

本项目旨在对时间序列分类的理论和实践进行变革。目前的技术水平无法处理描述人类面临的许多关键问题的大量时间序列，如疾病传播和气候变化。该项目寻求开发能够在全球范围内分析动态过程的方法，在给定的计算预算内提供可行的最准确的分类器。该项目的预期成果包括高效、有效和广泛适用的时间序列分类技术。这应该会为无数行业带来重大好处，将数据科学转变为解决时间序列问题的方法，并支持工业、商业和政府的创新。