Trying to identify most common distinct motifs in time series data and then search for those motifs #438
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Hi, I'm wondering anyone can possibly point me in the right direction, I'm reading the paper - https://www.researchgate.net/publication/343280455_Human_Presence_Detection_by_monitoring_the_indoor_CO2_concentration They mention: I believe this is one of the algorithms your API uses. The following is an example of one of their figures: I have used their open data and tried to use stumpy, to find the same first 3 motifs as them, by doing:
The following image shows my results using their data: The 3 motifs that the motifs function is finding for me, all seem to be very similar in that they're all below the downward trends of the CO2 ppm line. I'm just wondering if anyone could possibly point me in the right direction to find the same 3 unique motifs they have and to then search for those motifs across the time series data. I'm thinking the motifs function, might not be the function I should be using to find the most common distinct motifs? Many thanks! |
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@chrisruk Thank you for your question and welcome to the STUMPY community. Please be forewarned that the Having said that, it looks like what you've done seems fine. However, I could be wrong but your window size of |
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@chrisruk Thank you for your question and welcome to the STUMPY community. Please be forewarned that the
stumpy.motifs
function is still in the experimental stage and should be officially released in the upcoming v1.9.0 release. In the meantime, the API may still change.Having said that, it looks like what you've done seems fine. However, I could be wrong but your window size of
m = 10
appears to be smaller than the window size used in the paper. Also, can you please plot the raw CO2 time series along with the matrix profile computed usingstumpy.stump
(like what is shown here)? That will at least give you an idea of where the potential motifs are located within your time series (by look…