[ML] Trap zero variance in forecast confidence interval calculation causing error to be logged #107
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
tveasey
changed the title
tveasey
force-pushed
the
bug/trap-zero-variance
branch
from
dff5d5d to
b3f15f2
Compare
tveasey
added a commit
to tveasey/ml-cpp-1
that referenced
this pull request
Jun 8, 2018
…ausing error to be logged (elastic#107)
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
If all the values added to a model before a forecast are identical, it triggers the following error in the confidence interval when forecasting:
ERROR CTrendComponent.cc@67 Failed calculating confidence interval: Error in function boost::math::normal_distribution<double>::normal_distribution: Scale parameter is 0, but must be > 0 !, prediction = 0, variance = 0, confidence = 95. In fact, this error is harmless (since we take the correct action in this case).This change explicitly traps the problem case, i.e. creating a distribution with zero variance. It doesn't result in any change in behaviour, but does suppress the error.
Release note: Stops zero variance data from generating a log error in the forecast confidence interval