gdpq11

4 ms·

1.
by gdpq11 8y ago
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.
2.
by gdpq11 8y ago
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 ve
3.
by gdpq11 8y ago
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.
4.
by gdpq11 8y ago
Exactly
5.
by gdpq11 8y ago
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
6.
by gdpq11 8y ago
Thanks for the heads up! We've made the correction.
7.
by gdpq11 8y ago
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 w
8.
by gdpq11 8y ago
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+
9.
by gdpq11 8y ago
Shoot, sorry about that! Definitely a typo.
10.
by gdpq11 8y ago
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 toplin
11.
Show HN: Matrixprofile-ts – A Python library for timeseries motifs and discords(github.com)
95 points by gdpq11 8y ago|33 comments
12.
Matrixprofile-ts: a Python library for finding time series patterns and outliers(github.com)
1 points by gdpq11 8y ago|0 comments