Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot hand

Description

Efficient and effective methods for classifying massive time series data. 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.. Scheme: Discovery Projects. Field: 4605 - Data Management and Data Science. Lead: Prof Geoffrey Webb