Statistical learning from path observations (B01)

• Subject Area: Mathematics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 516748464

Statistics and machine learning are now successfully applied to extremely complex and high-dimensional problems, often with data having a natural path-like structure, such as time series. This project aims to explore the statistical properties of the theory of rough paths and compare it with other methods. We will use path signatures as feature maps in learning, incorporating them into standard classification methods and analyzing the data with algebraic and geometric methods. The goal is to create explainable classifiers with theoretical guarantees for practical applications.

• DFG Programme: CRC/Transregios
• Subproject of: TRR 388: Rough Analysis, Stochastic Dynamics and Related Fields
• Applicant Institution: Technische Universität Berlin
• Project Heads: Dr. Carlos Améndola Cerón; Privatdozent Dr. Christian Bayer; Professor Dr. Markus Reiß