DeepTime: Studying deep-learning architectures for time series classification
Abstract
This Project aims to develop deep learning architectures to classify time series at scale. This Project is motivated by new-generation satellites that are now imaging our planet frequently, completely, in high-resolution, and at no charge to end-users. This introduces unprecedented opportunities to monitor the flux of our Planet s systems. However, we cannot yet make the most of these opportunities, as state-of-the-art time series classification techniques do not scale tohandle such wealth of data, and are not capable of leveraging the spatial and spectral dimensions of the data. Deep learning technologies have recently revolutionized the research field of machine learning, and particularly for data with spatial or temporal structure. This Project seeks to develop new deep learning technologies that can make the most of the temporal, spatial and spectral dimensions of series of satellite images. This Project’s outputs will be both applicable to general time series and to the satellite application.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 28, 2018
- Source ID
- FA23861814030
Entities
People
- Francois Petitjean
Organizations
- Air Force Office of Scientific Research
- Monash University
- United States Air Force