Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Non-stationary environment] Auto_regression based on which arithmetic metric? #18

Open
brrrachel opened this issue Jun 18, 2020 · 1 comment
Assignees

Comments

@brrrachel
Copy link
Collaborator

brrrachel commented Jun 18, 2020

For producing series for each <component, failure> combination, there could be different ways to do this, e.g.:

  • mean value, as the average value,
  • mode value, as the most common value, and
  • median value, the value separating the higher half from the lower half.

The different values might have an influence on the outcoming data series. Which value would we expect to see?

@christianadriano
Copy link
Member

I think we could start using the mean first then later on we can compare the results using the mode and median.

The code do simulate non-stationary data is here:
http://web.vu.lt/mif/a.buteikis/wp-content/uploads/2019/02/02_StationaryTS_Python.html

@brrrachel could you please give a look and choose two options?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants