feat: integrate ModelPerformanceCallback and ConstantQuantileForecaster into workflows#879
Merged
Merged
Conversation
|
Signed-off-by: lschilders <lars.schilders@alliander.com>
Signed-off-by: lschilders <lars.schilders@alliander.com>
|
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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
This pull request introduces enhancements to the forecasting workflow configuration by adding a new model type and enabling model performance evaluation via a callback. The changes allow users to select a constant quantile forecaster and to configure a callback that checks model performance at the end of training against a configurable metric and threshold.
New model support:
ConstantQuantileForecasteras a selectable model type in theForecastingWorkflowConfig, allowing users to choose a model that predicts constant quantiles. [1] [2] [3]Model performance evaluation:
ModelPerformanceCallbackand made it available for use in both standard and ensemble forecasting workflows. This callback can be enabled via the configuration and will evaluate model performance at the end of fitting, based on a user-specified metric, direction, quantile, and threshold. [1] [2] [3] [4] [5] [6] [7]These changes improve the flexibility and robustness of the forecasting workflow by allowing more model selection options and providing automated performance checks.