Invariants of multidimensional time series based on their iterated-integral signature
arXiv:1801.06104v23 citations
Originality Synthesis-oriented
AI Analysis
This provides domain-specific tools for time series analysis, but appears incremental as it builds on existing signature methods.
The paper tackles the problem of extracting invariant features from multidimensional time series under transformations like rotations and permutations, using Chen's iterated-integral signature as a basis.
We introduce a novel class of features for multidimensional time series, that are invariant with respect to transformations of the ambient space. The general linear group, the group of rotations and the group of permutations of the axes are considered. The starting point for their construction is Chen's iterated-integral signature.