model_selection
¶
backtesting_forecaster(forecaster, y, steps, metric, initial_train_size=None, fixed_train_size=True, gap=0, skip_folds=None, allow_incomplete_fold=True, exog=None, refit=False, interval=None, n_boot=250, random_state=123, in_sample_residuals=True, binned_residuals=False, n_jobs='auto', verbose=False, show_progress=True)
¶
Backtesting of forecaster model.
 If
refit
isFalse
, the model will be trained only once using theinitial_train_size
first observations.  If
refit
isTrue
, the model is trained on each iteration, increasing the training set.  If
refit
is aninteger
, the model will be trained every that number of iterations.  If
forecaster
is already trained andinitial_train_size
isNone
, no initial train will be done and all data will be used to evaluate the model. However, the firstlen(forecaster.last_window)
observations are needed to create the initial predictors, so no predictions are calculated for them.
A copy of the original forecaster is created so that it is not modified during the process.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect)

Forecaster model. 
required 
y 
pandas Series

Training time series. 
required 
steps 
int

Number of steps to predict. 
required 
metric 
(str, Callable, list)

Metric used to quantify the goodness of fit of the model.

required 
initial_train_size 
int

Number of samples in the initial train split. If 
`None`

fixed_train_size 
bool

If True, train size doesn't increase but moves by 
`True`

gap 
int

Number of samples to be excluded after the end of each training set and before the test set. 
`0`

skip_folds 
(int, list)

If 
`None`

allow_incomplete_fold 
bool

Last fold is allowed to have a smaller number of samples than the

`True`

exog 
pandas Series, pandas DataFrame

Exogenous variable/s included as predictor/s. Must have the same
number of observations as 
`None`

refit 
(bool, int)

Whether to refit the forecaster in each iteration. If 
`False`

interval 
list

Confidence of the prediction interval estimated. Sequence of percentiles
to compute, which must be between 0 and 100 inclusive. For example,
interval of 95% should be as 
`None`

n_boot 
int

Number of bootstrapping iterations used to estimate prediction intervals. 
`500`

random_state 
int

Sets a seed to the random generator, so that boot intervals are always deterministic. 
`123`

in_sample_residuals 
bool

If 
`True`

binned_residuals 
bool


`False`

n_jobs 
(int, auto)

The number of jobs to run in parallel. If 
`'auto'`

verbose 
bool

Print number of folds and index of training and validation sets used for backtesting. 
`False`

show_progress 
bool

Whether to show a progress bar. 
`True`

Returns:
Name  Type  Description 

metric_values 
pandas DataFrame

Value(s) of the metric(s). 
backtest_predictions 
pandas DataFrame

Value of predictions and their estimated interval if

Source code in skforecast/model_selection/model_selection.py
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grid_search_forecaster(forecaster, y, param_grid, steps, metric, initial_train_size, fixed_train_size=True, gap=0, skip_folds=None, allow_incomplete_fold=True, exog=None, lags_grid=None, refit=False, return_best=True, n_jobs='auto', verbose=True, show_progress=True, output_file=None)
¶
Exhaustive search over specified parameter values for a Forecaster object. Validation is done using time series backtesting.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect)

Forecaster model. 
required 
y 
pandas Series

Training time series. 
required 
param_grid 
dict

Dictionary with parameters names ( 
required 
steps 
int

Number of steps to predict. 
required 
metric 
(str, Callable, list)

Metric used to quantify the goodness of fit of the model.

required 
initial_train_size 
int

Number of samples in the initial train split. 
required 
fixed_train_size 
bool

If True, train size doesn't increase but moves by 
`True`

gap 
int

Number of samples to be excluded after the end of each training set and before the test set. 
`0`

skip_folds 
(int, list)

If 
`None`

allow_incomplete_fold 
bool

Last fold is allowed to have a smaller number of samples than the

`True`

exog 
pandas Series, pandas DataFrame

Exogenous variable/s included as predictor/s. Must have the same
number of observations as 
`None`

lags_grid 
(list, dict)

Lists of lags to try, containing int, lists, numpy ndarray, or range
objects. If 
`None`

refit 
(bool, int)

Whether to refit the forecaster in each iteration. If 
`False`

return_best 
bool

Refit the 
`True`

n_jobs 
(int, auto)

The number of jobs to run in parallel. If 
`'auto'`

verbose 
bool

Print number of folds used for cv or backtesting. 
`True`

show_progress 
bool

Whether to show a progress bar. 
`True`

output_file 
str

Specifies the filename or full path where the results should be saved.
The results will be saved in a tabseparated values (TSV) format. If

`None`

Returns:
Name  Type  Description 

results 
pandas DataFrame

Results for each combination of parameters.

Source code in skforecast/model_selection/model_selection.py
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random_search_forecaster(forecaster, y, param_distributions, steps, metric, initial_train_size, fixed_train_size=True, gap=0, skip_folds=None, allow_incomplete_fold=True, exog=None, lags_grid=None, refit=False, n_iter=10, random_state=123, return_best=True, n_jobs='auto', verbose=True, show_progress=True, output_file=None)
¶
Random search over specified parameter values or distributions for a Forecaster object. Validation is done using time series backtesting.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect)

Forecaster model. 
required 
y 
pandas Series

Training time series. 
required 
param_distributions 
dict

Dictionary with parameters names ( 
required 
steps 
int

Number of steps to predict. 
required 
metric 
(str, Callable, list)

Metric used to quantify the goodness of fit of the model.

required 
initial_train_size 
int

Number of samples in the initial train split. 
required 
fixed_train_size 
bool

If True, train size doesn't increase but moves by 
`True`

gap 
int

Number of samples to be excluded after the end of each training set and before the test set. 
`0`

skip_folds 
(int, list)

If 
`None`

allow_incomplete_fold 
bool

Last fold is allowed to have a smaller number of samples than the

`True`

exog 
pandas Series, pandas DataFrame

Exogenous variable/s included as predictor/s. Must have the same
number of observations as 
`None`

lags_grid 
(list, dict)

Lists of lags to try, containing int, lists, numpy ndarray, or range
objects. If 
`None`

refit 
(bool, int)

Whether to refit the forecaster in each iteration. If 
`False`

n_iter 
int

Number of parameter settings that are sampled per lags configuration. n_iter trades off runtime vs quality of the solution. 
`10`

random_state 
int

Sets a seed to the random sampling for reproducible output. 
`123`

return_best 
bool

Refit the 
`True`

n_jobs 
(int, auto)

The number of jobs to run in parallel. If 
`'auto'`

verbose 
bool

Print number of folds used for cv or backtesting. 
`True`

show_progress 
bool

Whether to show a progress bar. 
`True`

output_file 
str

Specifies the filename or full path where the results should be saved.
The results will be saved in a tabseparated values (TSV) format. If

`None`

Returns:
Name  Type  Description 

results 
pandas DataFrame

Results for each combination of parameters.

Source code in skforecast/model_selection/model_selection.py
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bayesian_search_forecaster(forecaster, y, search_space, steps, metric, initial_train_size, fixed_train_size=True, gap=0, skip_folds=None, allow_incomplete_fold=True, exog=None, refit=False, n_trials=10, random_state=123, return_best=True, n_jobs='auto', verbose=True, show_progress=True, output_file=None, engine='optuna', kwargs_create_study={}, kwargs_study_optimize={})
¶
Bayesian optimization for a Forecaster object using time series backtesting and optuna library.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect)

Forecaster model. 
required 
y 
pandas Series

Training time series. 
required 
search_space 
Callable(optuna)

Function with argument 
required 
steps 
int

Number of steps to predict. 
required 
metric 
(str, Callable, list)

Metric used to quantify the goodness of fit of the model.

required 
initial_train_size 
int

Number of samples in the initial train split. 
required 
fixed_train_size 
bool

If True, train size doesn't increase but moves by 
`True`

gap 
int

Number of samples to be excluded after the end of each training set and before the test set. 
`0`

skip_folds 
(int, list)

If 
`None`

allow_incomplete_fold 
bool

Last fold is allowed to have a smaller number of samples than the

`True`

exog 
pandas Series, pandas DataFrame

Exogenous variable/s included as predictor/s. Must have the same
number of observations as 
`None`

refit 
(bool, int)

Whether to refit the forecaster in each iteration. If 
`False`

n_trials 
int

Number of parameter settings that are sampled in each lag configuration. 
`10`

random_state 
int

Sets a seed to the sampling for reproducible output. When a new sampler
is passed in 
`123`

return_best 
bool

Refit the 
`True`

n_jobs 
(int, auto)

The number of jobs to run in parallel. If 
`'auto'`

verbose 
bool

Print number of folds used for cv or backtesting. 
`True`

show_progress 
bool

Whether to show a progress bar. 
`True`

output_file 
str

Specifies the filename or full path where the results should be saved.
The results will be saved in a tabseparated values (TSV) format. If

`None`

engine 
str

Bayesian optimization runs through the optuna library. 
`'optuna'`

kwargs_create_study 
dict

Keyword arguments (key, value mappings) to pass to optuna.create_study(). If default, the direction is set to 'minimize' and a TPESampler(seed=123) sampler is used during optimization. 
`{}`

kwargs_study_optimize 
dict

Only applies to engine='optuna'. Other keyword arguments (key, value mappings) to pass to study.optimize(). 
`{}`

Returns:
Name  Type  Description 

results 
pandas DataFrame

Results for each combination of parameters.

best_trial 
optuna object

The best optimization result returned as a FrozenTrial optuna object. 
Source code in skforecast/model_selection/model_selection.py
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select_features(forecaster, selector, y, exog=None, select_only=None, force_inclusion=None, subsample=0.5, random_state=123, verbose=True)
¶
Feature selection using any of the sklearn.feature_selection module selectors
(such as RFECV
, SelectFromModel
, etc.). Two groups of features are
evaluated: autoregressive features and exogenous features. By default, the
selection process is performed on both sets of features at the same time,
so that the most relevant autoregressive and exogenous features are selected.
However, using the select_only
argument, the selection process can focus
only on the autoregressive or exogenous features without taking into account
the other features. Therefore, all other features will remain in the model.
It is also possible to force the inclusion of certain features in the final
list of selected features using the force_inclusion
parameter.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoreg, ForecasterAutoregCustom)

Forecaster model. 
required 
selector 
object

A feature selector from sklearn.feature_selection. 
required 
y 
pandas Series, pandas DataFrame

Target time series to which the feature selection will be applied. 
required 
exog 
pandas Series, pandas DataFrame

Exogenous variable/s included as predictor/s. Must have the same
number of observations as 
`None`

select_only 
str

Decide what type of features to include in the selection process.

`None`

force_inclusion 
(list, str)

Features to force include in the final list of selected features.

`None`

subsample 
(int, float)

Proportion of records to use for feature selection. 
`0.5`

random_state 
int

Sets a seed for the random subsample so that the subsampling process is always deterministic. 
`123`

verbose 
bool

Print information about feature selection process. 
`True`

Returns:
Name  Type  Description 

selected_autoreg 
list

List of selected autoregressive features. 
selected_exog 
list

List of selected exogenous features. 
Source code in skforecast/model_selection/model_selection.py
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