Discovering Leitmotifs in Multidimensional Time Series
Project description
Motiflets
This page was built in support of our paper "Discovering Leitmotifs in Multidimensional Time Series" by Patrick Schäfer and Ulf Leser.
Supporting Material
tests
: Please see the python tests for use casesnotebooks
: Please see the Jupyter Notebooks for use casescsvs
: The results of the scalability experimentsmotiflets
: Code implementing multidimensonal k-Motifletdatasets
: Use cases in the paper
k-Motiflets
TODO
Showcase
TODO
Installation
The easiest is to use pip to install motiflets.
a) Install using pip
pip install leitmotif
You can also install the project from source.
b) Build from Source
First, download the repository.
git clone https://github.com/patrickzib/motiflets.git
Change into the directory and build the package from source.
pip install .
Usage
Here we illustrate how to use k-Motiflets.
Use Cases
Data Sets: We collected challenging real-life data sets to assess the quality and scalability of MD algorithms. An overview of datasets can be found in Table 2 of our paper.
TODO
- Jupyter-Notebook Univariate Use Cases for k-Motiflets: highlights all use cases used in the paper and shows the unique ability of k-Motiflets to learn its parameters from the data and find itneresting motif sets.
Citation
If you use this work, please cite as:
TODO
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for leitmotif-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f41be93c17eeacec83c5b166e27d1b86cbba0343a086ec62800c62b9f2798eb0 |
|
MD5 | e662ff921de2bc80c73261acab2faa41 |
|
BLAKE2b-256 | f3f5ebf26bb49416816b226ee46ae16a0c4a638d81201975d31330fc08be059d |