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LEIT-motifs

Scalable discovery of multidimensional motifs in time series.

The EvaluationSuite Jupiter Notebook offer an interactive small evaluation against the SoTA for exact discovery other than many usage examples without the need to download anything. For further evaluation refer to source/test.py
The Thesis_Plots notebook ensures repeatability for all the figures included in the thesis.

Implementation detals

Discretized Random Projections has been implemented with the use of tensoring to minimize the computations. MinHash is currently offered by the datasketch package.

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Scalable mining of multidimensional time series motifs.

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