This thesis consists of two parts. The first part (Chapters 2-4) focuses on the problem of inverting the signature of a path of bounded variation, and we present three results here. First, we give an explicit inversion formula for any axis path in terms of its signature. Second, we show that for relatively smooth paths, the derivative at the end point can be approximated arbitrarily closely by its signature sequence, and we provide explicit error estimates. As an application, we give an effective inversion procedure for piecewise linear paths. Finally, we prove a uniform estimate for the signatures of paths of bounded variations, and obtain a reconstruction theorem via that uniform estimate. Although this general reconstruction theorem is not computationally efficient, the techniques involved in deriving the uniform estimate are useful in other situations, and we also give an application in the case of expected signatures for Brownian motion. The second part (Chapter 5) deals with rough paths. After introducing proper backgrounds, we extend the uniform estimate above to the context of rough paths, and show how it can lead to simple proofs of distance bounds for Gaussian iterated integrals.

This thesis explores a new way to analyze spatiotemporal data. By combining topology, the path signature and machine learning a robust model to analyze swarming behavior over time is created. Using persistent homology a representation of spatial data is obtained and the path signature gives us a representation for how this changes over time. This representation allows us to compare samples even if they have different amounts of time steps and different length of the sequence. It is also resistant to noise in the spatial representation. Using this data is then used to train a gaussian process regressor to extract parameters that govern the movement of swarms. Our analysis shows that the tested method is a good candidate for analyzing spatiotemporal data and that it warrants further studies. / Detta examensarbete utforskar ett nytt sätt att analysera spatiotemporal data. Genom att kombinera topologi, vägsignaturer och maskininlärning skapas en robust modell för att analysera svärmar beter sig över tid. Genom persistent homology erhålls en representation av spatial data och dess vägsignatur ger oss en representation för hur detta förändras över tiden. Denna representation gör det möjligt för oss att jämföra data även om de har olika antal tidssteg och sekvenserna är olika långa. Den är också motståndskraftig mot brus i den spatiala representationen. Denna data används sedan för att träna en gaussisk process-regressor för att extrahera parametrar som styr svärmarnas rörelse. Vår analys visar att den testade metoden är en bra kandidat för att analysera spatiotemporal data och att den är värd att studera ytterligare.