Show HN: Matrixprofile-ts – A Python library for timeseries motifs and discords(github.com)
github.com
Show HN: Matrixprofile-ts – A Python library for timeseries motifs and discords
https://github.com/target/matrixprofile-ts
13 comments
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)
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.
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.
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.
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.
Cool. Thanks for responding. I'll provide some example code and my specific use case.
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.
See also the tutorial slides available here https://www.cs.ucr.edu/~eamonn/MatrixProfile.html
Interested folks may also want to check out the Python library nupic for streaming time-series analytics [1] and anomaly detection [2].
[1] https://github.com/numenta/nupic
[2] https://www.sciencedirect.com/science/article/pii/S092523121...
[1] https://github.com/numenta/nupic
[2] https://www.sciencedirect.com/science/article/pii/S092523121...
It seems like the method assumes stationarity, can anyone comment on how this might be useful if one has brown spectrum?
It also seems like it requires some normalization of the data, this should counted as an effective parameter of the method.
In any case, it’d be useful to a GLM with these profiles against bug or outage reports, log rates etc.
It also seems like it requires some normalization of the data, this should counted as an effective parameter of the method.
In any case, it’d be useful to a GLM with these profiles against bug or outage reports, log rates etc.
It is possible that two occurrences of the same motif can overlap.
And
It is possible that two different motifs can overlap.
Lets see both cases, in string analogs. We will start with the second case, using an example from John Cleese…
“…itself…and hence the very meaning of life itselfish bastard, I'll kick him… selfish…” Here there is a motif “itself” and there is a motif “selfish”. Note that one occurrence of each motif appears overlapping in “itselfish”. --- Now for the first case: “….soihsehihrhewCOMICOMICireoqiwwherhqwe…”
Here we have a motif “COMIC”, but they share a letter, the central ‘C’. We can allow motifs to share more letters, but they cannot share ALL letters, that would be a trivial match.
The matrix profile has a simple parameter (the exclusion zone) that lets you control how much overlap you want to allow.
“…itself…and hence the very meaning of life itselfish bastard, I'll kick him… selfish…” Here there is a motif “itself” and there is a motif “selfish”. Note that one occurrence of each motif appears overlapping in “itselfish”. --- Now for the first case: “….soihsehihrhewCOMICOMICireoqiwwherhqwe…”
Here we have a motif “COMIC”, but they share a letter, the central ‘C’. We can allow motifs to share more letters, but they cannot share ALL letters, that would be a trivial match.
The matrix profile has a simple parameter (the exclusion zone) that lets you control how much overlap you want to allow.
Assuming this to be the reply to my question:
I probably was a bit imprecise but what I want to know is if there is a way to apply this to data that are possibly in a superposition and overlapping meaning that you only see the sum of the events. For example if one wants to analyze a changing electric or magnetic field.
Nevertheless, the points you mentioned are something I did not think about at first, interesting once again.
I probably was a bit imprecise but what I want to know is if there is a way to apply this to data that are possibly in a superposition and overlapping meaning that you only see the sum of the events. For example if one wants to analyze a changing electric or magnetic field.
Nevertheless, the points you mentioned are something I did not think about at first, interesting once again.
Thanks for making this available! As someone who also has some TS analysis to do, I appreciate the fact that the code is available and in Python!
I am curious: are you affiliated with the UCR people? What's your opinion on Keogh's claims of the matrix profile making many TS problems easy or trivial?
I am curious: are you affiliated with the UCR people? What's your opinion on Keogh's claims of the matrix profile making many TS problems easy or trivial?
I'm not affiliated with UCR, though I am a product of the UC system :)
I agree with Keogh that Matrix Profile can help solve a very wide range of problems, but you usually have to go a little bit deeper than just calculating the topline Matrix Profile. A good example of this is that if you calculate the Matrix Profile for something with daily seasonality (say, in-store retail sales), you'll see the same daily pattern in the Matrix Profile. The straightforward fix for this is to normalize by time window (say, only compare the Matrix Profile at the same time each day).
I agree with Keogh that Matrix Profile can help solve a very wide range of problems, but you usually have to go a little bit deeper than just calculating the topline Matrix Profile. A good example of this is that if you calculate the Matrix Profile for something with daily seasonality (say, in-store retail sales), you'll see the same daily pattern in the Matrix Profile. The straightforward fix for this is to normalize by time window (say, only compare the Matrix Profile at the same time each day).
Thanks for making this available.
Slight beginner question pertaining to the anomaly detection with STAMPI example: How exactly do the graphs showcase a "detection" by the Matrix Profile?
While the signal graph is clearly out of bounds (100% above last upper bound), the relativ Matrix Profile's "spike in value" fits perfectly within the bounds of that graph.
Slight beginner question pertaining to the anomaly detection with STAMPI example: How exactly do the graphs showcase a "detection" by the Matrix Profile?
While the signal graph is clearly out of bounds (100% above last upper bound), the relativ Matrix Profile's "spike in value" fits perfectly within the bounds of that graph.
Yeah, this is actually a good example of why it's important to add a bit to the raw Matrix Profile. The point is anomalous with respect to the pattern preceding it (the "sawtooth"), so in this case one needs to consider the whole Matrix Profile. It's a good callout in that the graph isn't a complete anomaly detection system; it more demonstrates how a single anomalous point can impact the Matrix Profile value.
Great, thanks for the context!
Can this be used for multivariate time series? If so, do you have an example?
Would this be along those lines? https://www.cs.ucr.edu/%7Eeamonn/Motif_Discovery_ICDM.pdf
Exactly
It definitely can be (I believe one of the academic papers covers that), but we haven't implemented anything yet. But it's definitely on the to-do list
Thanks, very interesting!
So what about superpositions of events i.e. two motifs overlapping, anyone any thoughts on that?
I guess this just means a lot more swiping of observed patterns over the timeseries and thus being somewhat slow.
So what about superpositions of events i.e. two motifs overlapping, anyone any thoughts on that?
I guess this just means a lot more swiping of observed patterns over the timeseries and thus being somewhat slow.
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Should the URL maybe be changed to https://github.com/target/matrixprofile-ts? I'm interested in the code, not in the "stargazers".
Shoot, sorry about that! Definitely a typo.
The funny thing is that I wanted to know more about the project after seeing this page. I'm not sure I would have continued if it was just another time series library.
No problem! We've updated the link.
Astonishingly, we can process 20 years’ worth of data, sampled every five minutes, in less than 20 seconds.
That sounds quite promising.
[1] https://tech.target.com/2018/12/11/matrix-profile.html