Comments (1)
Great question! (and devastating penalty shootout for France a few days ago..)
In this case, you would make multiple forecasts with greykite (and any other library) and then apply your own method to generate the consensus forecasts.
It might be possible to stretch the usage of ReconcileAdditiveForecasts
to generate such consensus, by creating a "tree" where each parent has a single child, but we haven't tried it ourselves. The consensus forecast would then be a linear combination of the input forecasts. See #25 for a brief description of that method if interested. Before going too deep into this, I'd recommend starting with a simpler method like median, as you mentioned, and making sure your input forecast are good.
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Related Issues (20)
- GreyKite 0.3.0 Library Issue HOT 7
- Library import issues HOT 1
- ValueError: ``MULTISTAGE_EMPTY`` can not be used without overriding. HOT 2
- Request to release tag 0.5.1 HOT 4
- Unable to get greykite 0.5.0 HOT 4
- Unable to run codes.
- Regressors Already Forecastd, No Lag Needed. But, getting warning "RuntimeWarning: Input data has many null values. Missing 21.45% of one input." HOT 1
- Lower Python version requirement to allow 3.8.10 HOT 1
- `design_info` is needed to make predictions on new data HOT 4
- Dealing with lot of 0s (zeroes) in Greykite Multistage Forecasting HOT 1
- mutable default <class 'greykite.framework.templates.autogen.forecast_config.ModelComponentsParam'> for field SILVERKITE is not allowed: use default_factory HOT 1
- MLFLOW support for the SilverKite Algorithm HOT 1
- Greykite design question
- Version 0.5.0 - broken macOS support with M1 chip due to incorrect version of pmdarima dependency
- Question: How to use sample_weight in grid searching a model HOT 2
- TypeError in docs examples HOT 1
- Code not working After Update
- TypeError on forecaster.run_forecast_config (same as the others) HOT 2
- ChangePoint detection for not daily data
- Using conda for installation
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