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 cases
• notebooks: Please see the Jupyter Notebooks for use cases
• csvs: The results of the scalability experiments
• motiflets: Code implementing multidimensonal k-Motiflet
• datasets: Use cases in the paper

k-Motiflets

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Showcase

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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.

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• 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:

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