A multi-dimensional stream and its signature representation

Abstract

The signature of a path is an essential object in the theory of rough paths. The signature representation of the data stream can recover standard statistics, e.g. the moments of the data stream. The classification of random walks indicates the advantages of using the signature of a stream as the feature set for machine learning.

References in corpus (1)

• Discretely sampled signals and the rough Hoff process

Cited by in corpus (2)

• Learning stochastic differential equations using RNN with log signature features
• Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition