-
Notifications
You must be signed in to change notification settings - Fork 4.5k
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
New parameter added in order to replace negative predictions with 0 #2610
base: main
Are you sure you want to change the base?
Conversation
Hi @mrtergl! Thank you for your pull request and welcome to our community. Action RequiredIn order to merge any pull request (code, docs, etc.), we require contributors to sign our Contributor License Agreement, and we don't seem to have one on file for you. ProcessIn order for us to review and merge your suggested changes, please sign at https://code.facebook.com/cla. If you are contributing on behalf of someone else (eg your employer), the individual CLA may not be sufficient and your employer may need to sign the corporate CLA. Once the CLA is signed, our tooling will perform checks and validations. Afterwards, the pull request will be tagged with If you have received this in error or have any questions, please contact us at [email protected]. Thanks! |
Thank you for signing our Contributor License Agreement. We can now accept your code for this (and any) Meta Open Source project. Thanks! |
1 similar comment
Thank you for signing our Contributor License Agreement. We can now accept your code for this (and any) Meta Open Source project. Thanks! |
This improvement can solve issue #2471 |
Hey @tcuongd , could you please review my pull request? |
I think this pull request is useful. I am currently using Prophet for various purposes. I currently use it as a code to replace negative values with 0 in the post-processing process. Because even if I do not train Prophet with values less than 0, there were cases where the prediction result was predicted as a negative number less than 0. So I personally think this suggestion can be useful. |
Sometimes, data series contain many values close to zero but are not expected to fall below zero. For example, a data frame with response times will likely have many near-zero values. In such cases, Prophet may produce yhat_lower or trend_lower metric values that are negative.
To address this issue, I have implemented a flag that sets all negative values to zero in the data frame. This flag can be configured when defining the model.