Jmotif implements SAX and SAX-VSM algorithms targeting interpretable time series classification. This approach aids in knowledge discovery by enabiling comparative studies of time series generated by different processes, or by the same process under different conditions.

Here is an example of SAX-VSM application to the well studied MNIST dataset (10 classes of time series representing handwritten digits) illustrating the algorithms's rotational invariance, robustness, and the capacity of characteristic features discovery and ranking. I have applied SAX-VSM to a small subset of the most divergent digits from MNIST train dataset with SAX parameters of sliding window 190, PAA 15, and Alphabet 5:

The background heatmap under each digit shows the patterns (190 points sliding window) locations and their weighting by color. While highlighting the most relevant sliding window positions, this visualization does not account for pattern's internal structure.

Digits at this figure are heatmap-like colored. This visualization highlights their particular features which were found as the most relevant to their class by SAX-VSM.

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