[jamesb93]

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.

[gdpq11]

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.

[jamesb93]

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?

[gdpq11]

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?

[jamesb93]

I used the pip version.

[jamesb93]

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