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.
- Tech Mahindra Partners With SpaceXSI to Bring AI Into Software Engineering
- Infosys AI Revenue Rises 29% in Q2, FY27 Growth Guidance Cut
- LTM Launches BlueVerse Industrial AI Lab in Chennai
- Chariot Launches Speech Recognition Model for 10 Indian Languages
- Meta Expects Up To $145 Billion In Capex As AI Investments Expand
- Cyient's Technology Order Intake Rises 39.7% as It Expands AI Capabilities