Sean Law, principal data scientist at Charles Schwab and the creator of Stumpy, announced the release of the latest version 1.11.0. The new version includes the oft-requested Minkowski (p-norm) Distance, support for Multi-dimensional Motif Discovery, new Annotation vector tutorials, and enhancements for Pan Matrix Profiles. The latest version is available on GitHub.

Stumpy is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of time series data mining tasks such as:

• pattern/motif (approximately repeated subsequences within a longer time series) discovery
• anomaly/novelty (discord) discovery
• shapelet discovery
• semantic segmentation
• streaming (on-line) data
• fast approximate matrix profiles
• time series chains (temporally ordered set of subsequence patterns)
• snippets for summarising long time series
• pan matrix profiles for selecting the best subsequence window size(s)

With 30K+ downloads, over 1K+ Github stars, and 100% code coverage, Stumpy is a favourite of academics, data scientists, software developers, and time-series enthusiasts. 
Check out the latest documentation or explore the hands-on tutorials.