Forecaster Object Globals
In scalecast, when reading documentation, it is helpful to know the following terms:
_estimators_
,_can_be_tuned_
,_cannot_be_tuned_
,_sklearn_estimators_
,_metrics_
,_determine_best_by_
,_normalizer_
.
_estimators_
these are the the models that forecast and can be set by using
f.set_estimator(...)
they come from popular machine learning libraries like scikit-learn, keras, statsmodels, and others
here is a list of all:
[1]:
from scalecast.Forecaster import _estimators_
print(*_estimators_,sep='\n')
arima
combo
elasticnet
gbt
hwes
knn
lightgbm
lstm
mlp
mlr
prophet
rf
rnn
silverkite
svr
xgboost
_can_be_tuned_
the following estimators can be tuned:
[2]:
from scalecast.Forecaster import _can_be_tuned_
print(*_can_be_tuned_,sep='\n')
arima
elasticnet
gbt
hwes
knn
lightgbm
mlp
mlr
prophet
rf
silverkite
svr
xgboost
_cannot_be_tuned_
the following cannot be tuned:
[3]:
from scalecast.Forecaster import _cannot_be_tuned_
print(*_cannot_be_tuned_,sep='\n')
combo
lstm
rnn
_sklearn_estimators_
these all come from scikit-learn or use a scikit-learn API and behave similarly (including being easy-to-tune, accepting a
normalizer
argument, and accpeting anXvars
argument):
[4]:
from scalecast.Forecaster import _sklearn_estimators_
print(*_sklearn_estimators_,sep='\n')
elasticnet
gbt
knn
lightgbm
mlp
mlr
rf
svr
xgboost
_metrics_
these are all the metrics available to use for model validation
f.set_validation_metric(...)
:
[5]:
from scalecast.Forecaster import _metrics_
print(*_metrics_,sep='\n')
r2
rmse
mape
mae
_determine_best_by_
these are all the metrics available to use for model comparison and sorting models best-to-worst
f.export(determine_best_by=...)
:
[6]:
from scalecast.Forecaster import _determine_best_by_
print(*_determine_best_by_,sep='\n')
TestSetRMSE
TestSetMAPE
TestSetMAE
TestSetR2
InSampleRMSE
InSampleMAPE
InSampleMAE
InSampleR2
ValidationMetricValue
LevelTestSetRMSE
LevelTestSetMAPE
LevelTestSetMAE
LevelTestSetR2
_normalizer_
these are all the options to scale your data when using an sklearn estimator
f.manual_forecast(normalizer=...)
:
[7]:
from scalecast.Forecaster import _normalizer_
print(*_normalizer_,sep='\n')
minmax
normalize
scale
pt
None
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