Persistent homology for time series data in Python

This repo contains

• A presentation explaining the basics of persistent homology, as well as some applications to time series data
• A notebook containing examples using the Dionysus and Ripser libraries to compute Betti numbers for time series data using both Pandas and PySpark. The computations utilize the additions of the Pandas UDF functionality introduced in Spark 2.3.

For more detailed discussion of homology persistent homology, see blog posts

• https://eric-bunch.github.io/blog/calculating_homology_of_simplicial_complex
• https://eric-bunch.github.io/blog/topological-data-analysis-and-persistent-homology