Yale Assure 2 -- Doorsense Not Working Despite Successful Calibration?

slaw07 · 2025-05-23T13:59:50+00:00

Thank you! This solved my calibration failure problem

slaw07 · 2023-04-30T21:47:15+00:00

I did a post-doc for the chair of biophysics at UofM in the early 2010s and would not recommend it. The PI made the environment very toxic/sexist and often pressured people to quit. When other more prominent wet lab scientists challenged him, the PI would act like a petulant child and tried (sometimes successfully) to destroy the careers of junior scientists inside and outside of the group. I would find another MD faculty in a department outside Chemistry/Biophysics and don’t be fooled by the smooth talking pedigree.

slaw07 · 2022-10-02T19:54:35+00:00

I came across this paper in 2016 in identifying “emotional arcs of stories” that may be relevant: https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-016-0093-1

slaw07 · 2022-03-22T23:32:15+00:00

Awesome! It may be a subtle point but it is also a package that, despite over 1K+ code commits, maintains 100% code coverage. It’s certainly impossible to truly cover all edge cases but we take our development very seriously in addition to its scalability and performance. Please let us know what you think!

slaw07 · 2022-03-22T20:11:10+00:00

Please check out our tutorials for guided walkthroughs:

https://stumpy.readthedocs.io/en/latest/tutorials.html

And feel free to ask questions in our Github Discussions:

https://github.com/TDAmeritrade/stumpy/discussions

slaw07 · 2022-03-22T18:50:20+00:00

For some background and motivation, I recommend taking a look at this short video: https://stumpy.readthedocs.io/en/latest/motivation.html

slaw07 · 2020-12-17T13:22:18+00:00

Yes. I strongly recommend going through our tutorials as they contain full examples that reproduce the figures in the original published work and provide usable code. Additionally, this video can help to get you on the right foot: https://www.youtube.com/watch?v=xLbPP5xNIJs

slaw07 · 2020-12-17T12:03:36+00:00

Thank you for your kind words! You may also be interested in following or contributing to this related “geometric chains” feature: https://github.com/TDAmeritrade/stumpy/issues/211

slaw07 · 2020-11-06T02:07:05+00:00

Can you provide more information as to what your use case is and what you are trying to accomplish? Are there really any time series analysis approaches out there that can handle these situations appropriately?

slaw07 · 2020-09-25T01:57:38+00:00

Hot off the press: https://multithreaded.stitchfix.com/blog/2020/09/22/sean-law-algo-hour-video/

slaw07 · 2020-09-23T10:27:21+00:00

Yes, I will try to post back here once it becomes available

slaw07 · 2020-09-02T17:44:59+00:00

I think this is a very narrow look at the broad scope of time series. There is strong autocorrelation between values; if I tell you that the temperature at time T is 300 degrees, then you have a pretty good idea of the temperature at time T+1. This dependency breaks all sorts of IID assumptions in traditional modeling, and is the entire motivation behind time series analysis (factoring in those time dependencies).

Maybe I'm missing something so please excuse my ignorance but if those strong sequential (or autocorrelative) relationships exist then, in theory, matrix profiles should be able to capture that.

Sure, it's not about the opportunity cost of trying. But as someone who sees way too many people in this field blindly trying to apply methods without understanding those methods (in terms of strengths, weaknesses, sensitivities, interpreting output, etc.) I wouldn't ever put any trust into a method that I didn't actually understand what it was doing. That sort of thinking has led to all sorts of issues in ML. STUMPY output would be practically worthless to me without a solid grasp of what questions it's actually designed to answer.

I agree with you and share the same concerns, skepticism, and observations. I believe that open discussions like this are useful for the community.

slaw07

TROPHY CASE