We tested the performance of computing the exact matrix profile using the Numba JIT compiled version of the code on randomly generated time series data with various lengths (i.e., np.random.rand(n)) along with different CPU and GPU hardware resources. For a more detailed example of how to use STUMPY please consult the latest documentation or explore our hands-on tutorials. In order to fully understand and appreciate the underlying algorithms and applications, it is imperative that you read the original publications. To install stumpy from source, see the instructions in the documentation.

Typical usage (1-dimensional time series data) with STUMP: Below, you will find code snippets that quickly demonstrate how to use STUMPY.

Please see our API documentation for a complete list of available functions and see our informative tutorials for more comprehensive example use cases. Whether you are an academic, data scientist, software developer, or time series enthusiast, STUMPY is straightforward to install and our goal is to allow you to get to your time series insights faster. Pan matrix profiles for selecting the best subsequence window size(s) Snippets for summarizing long time series Time series chains (temporally ordered set of subsequence patterns) Pattern/motif (approximately repeated subsequences within a longer time series) discovery What’s important is that once you’ve computed your matrix profile (middle panel above) it can then be used for a variety of time series data mining tasks such as: STUMPY is a powerful and scalable Python library that efficiently computes something called the matrix profile, which is just an academic way of saying “for every (green) subsequence within your time series, automatically identify its corresponding nearest-neighbor (grey)”:

Stumpy pencil brush