Comment by kiv6 | Hacker News Reader

Comment by kiv6 | Hacker News Reader

Thanks for publishing!

I'd love to hear more details about how this is used in production. For example, if you have an anomaly that occurs only twice in a long dataset, the two anomalies would match each other and would have a low matrix profile value and be considered equally normal as a pattern that recurs thousands of times, correct?

I would normally think of an anomaly as a point in a low density region of space, but Matrix Profile seems to have a more strict definition as a point that has a large distance to its nearest neighbour - is that fair?

I'm also interested in your process for setting the parameter of the subquery length. Do you have to already know something about the expected length of an anomaly/motif, or do you sweep over multiple values?

How does this tie into alerting? Do you set a threshold on the matrix profile value that would fire an automated alert? Or is this used more as an offline tool to explore the dataset?

Minor nitpick: on the Target blog post, Prof. Keogh's name is spelled wrong (as Keough)

The nice thing about how the Matrix Profile is built is that you can slice up different regions of time to focus on your use case. To build the MP you start with an NxN matrix that lists the distance between every point (or technically N-m+1 x N-m+1), then find the overall closest distance for each point. However, we've found that first "updating" the NxN matrix allows you to do analyses like your two anomaly example.

In that case, you'd create a parameter "w" that specifies the boundary between when two matching points are a pattern, or if enough time has elapsed so that they should be considered two anomalies. In the NxN matrix, for the ith row you'd then set every value outside the i+w/i-w boundary to infinity. In that way, the resulting Matrix Profile would account for your situation.

Due to the algorithm's speed we do often sweep over multiple values, but try to use domain knowledge where we can. And for alerting, we sometimes have labeled data that we can calibrate the threshold to, but often times that's a matter of customer trial and error.

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.

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.

Does this help.. https://www.cs.ucr.edu/~eamonn/public/GPU_Matrix_profile_VLD...

Definitely! We see GPUs as the primary method of calculating the Matrix Profile given that Big Data is only getting bigger. So, advancing from STOMP-GPU to SCAMP will be very helpful.

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