NormalHedgeEnsemble#
- class NormalHedgeEnsemble(n_estimators=10, a=1, loss_func=None)[source]#
Parameter free hedging algorithm.
Implementation of A Parameter-free Hedging Algorithm, Kamalika Chaudhuri, Yoav Freund, Daniel Hsu (2009) as a hedge-style algorithm.
- Parameters:
- n_estimatorsfloat
number of estimators
- Tint
forecasting horizon (in terms of timesteps)
- afloat
normalizing constant
- loss_funcfunction
loss function which follows sklearn.metrics API, for updating weights
Methods
update
(y_pred, y_true[, low_c])Update forecaster weights.
- update(y_pred, y_true, low_c=0.01)[source]#
Update forecaster weights.
The weights are updated over the estimators by passing previous observations and updating based on Normal Hedge.
- Parameters:
- y_prednp.array(), shape=(time_axis,estimator_axis)
array with predictions from the estimators
- y_truenp.array(), shape=(time_axis)
array with actual values for predicted quantity