Comments (2)
Hi @ErikWitt ,
Thx for your interest in ruptures
!
You can have a look at this page of the docs. Might not be exactly what you are looking for, but it gives some intuition on a possible approach if you want quantify the relative importance of change points.
Hope it helps !
from ruptures.
Hi @ErikWitt,
Unfortunately, computing a significance level requires some assumption on the data, and even in this situation, it remains complex.
Within ruptures, what you can do is do change-point detection with a penalty (with Pelt
or KernelCPD
). The penalty will threshold out all change-points with a low amplitude shift. However, setting a relevant penalty level can only be done manually.
Outside of ruptures, they are several articles that you can look at:
- Post-selection inference (mostly valid for Gaussian signals) https://www.stat.cmu.edu/~ryantibs/papers/binseginf.pdf
- computing a probabilty of being a change-point (https://doi.org/10.1007/s00180-013-0422-9). This can be quite general but the implementation is a bit difficult.
Hope this helps
from ruptures.
Related Issues (20)
- Docs: Cost function illustrations HOT 1
- multidimensional data HOT 2
- More metrics ? e.g. adjusted randindex HOT 2
- [BUG] Two badges not rendering correctly in readme HOT 2
- How to `set_xticklabels` HOT 1
- Piecewise linear HOT 2
- Provide Python 3.11 wheels HOT 1
- Algorithm and threshold advice for piecewise linear
- Rand index over 1 HOT 1
- Pruning rule in Pelt HOT 1
- Normalization and Cost HOT 3
- Clarification on Breakpoint Indexing with Dataframe Input HOT 2
- Python 3.12 wheels HOT 4
- Unable to install in Poetry 1.7.1 HOT 3
- Breakpoints found in DataFrame but not in NumpyArray with same data HOT 3
- Question: How do i use Ruptures to detect large data streaming? HOT 2
- Speed it up HOT 1
- ruptures got 2 out of 3 change points correct HOT 1
- BadSegmentationParameters: HOT 5
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from ruptures.