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You say that the algorithm is fast, and the literature certainly points to this too but I tried the python implementation (linked here) on some audio data sets. 1 second of audio at a reasonable quality is 44.1k data points and it was taking minutes to process this data.

I tried an R implementation which was multi-threaded and a lot faster, but still the algorithm took ages to test lots of different window sizes and data sets.

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That's odd, we've definitely processed larger datasets much quicker than that. Feel free to raise an issue on Github and we can take a look.

Cool. Thanks for responding. I'll provide some example code and my specific use case.

Just to advance my previous comment; right now the most interesting feature to me is the motif detection, however, the motif's are always incredibly short (or a fixed size?). Please excuse me ignorance in this regard, I'm not too versed in the algorithm. Is there any use cases where you have looked at longer motifs?

There's some interesting work around multi-month seasonality that covers what you're talking about, but Eamonn Keogh would probably have a better answer that I :). Also, in regards to your performance issue, did you use the pip version or the code directly from Github?

I used the pip version.

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