Skip to main content

Time-series Visualization with the Matrix Profile and Multidimensional Scaling.

Project description


Visualization of Time-Series with the Matrix Profile and Multidimensional Scaling - a Python Implementation.


Multidimensional scaling is an algorithm that projects a set of objects represented by their distance matrix into a lesser dimensional space, such that the pairwise distance is preserved as much as possible. If we project onto a 1-dimensional or a 2-dimensional space, we can even plot the resultant projections, and visually understand the similarity between these objects.
At a first glance, this seems applicable to time-series as well, where we want to capture similar regions across the entire time-series.
However, since we do not know apriori where our regions of interest are, we can simply select all subsequences of a given length from the original time-series.
Let's try this out!


Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series.
Chin-Chia Michael Yeh, Helga Van Herle, and Eamonn Keogh. IEEE ICDM 2016.

Project details

Release history Release notifications | RSS feed

This version


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for tsvisualize, version 1.0
Filename, size File type Python version Upload date Hashes
Filename, size tsvisualize-1.0-py2-none-any.whl (5.2 kB) File type Wheel Python version py2 Upload date Hashes View
Filename, size tsvisualize-1.0.tar.gz (3.8 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page