model_selection_multiseries
¶
backtesting_forecaster_multiseries(forecaster, series, steps, metric, initial_train_size, fixed_train_size=True, gap=0, allow_incomplete_fold=True, levels=None, exog=None, refit=False, interval=None, n_boot=500, random_state=123, in_sample_residuals=True, n_jobs='auto', verbose=False, show_progress=True)
¶
Backtesting for multiseries and multivariate forecasters.
 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 
(ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate)

Forecaster model. 
required 
series 
pandas DataFrame

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`

allow_incomplete_fold 
bool

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

`True`

levels 
(str, list)

Time series to be predicted. If 
`None`

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. If 
`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`

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 

metrics_levels 
pandas DataFrame

Value(s) of the metric(s). Index are the levels and columns the metrics. 
backtest_predictions 
pandas DataFrame

Value of predictions and their estimated interval if

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

forecaster 
(ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate)

Forecaster model. 
required 
series 
pandas DataFrame

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`

allow_incomplete_fold 
bool

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

`True`

levels 
(str, list)

level ( 
`None`

exog 
pandas Series, pandas DataFrame

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

lags_grid 
list of int, lists, np.narray or range

Lists of 
`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

Returns:
Name  Type  Description 

results 
pandas DataFrame

Results for each combination of parameters.

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

forecaster 
(ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate)

Forecaster model. 
required 
series 
pandas DataFrame

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`

allow_incomplete_fold 
bool

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

`True`

levels 
(str, list)

level ( 
`None`

exog 
pandas Series, pandas DataFrame

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

lags_grid 
list of int, lists, np.narray or range

Lists of 
`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

Returns:
Name  Type  Description 

results 
pandas DataFrame

Results for each combination of parameters.

Source code in skforecast\model_selection_multiseries\model_selection_multiseries.py
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backtesting_forecaster_multivariate(forecaster, series, steps, metric, initial_train_size, fixed_train_size=True, gap=0, allow_incomplete_fold=True, levels=None, exog=None, refit=False, interval=None, n_boot=500, random_state=123, in_sample_residuals=True, n_jobs='auto', verbose=False, show_progress=True)
¶
This function is an alias of backtesting_forecaster_multiseries.
Backtesting for multiseries and multivariate forecasters.
If refit
is False, the model is trained only once using the initial_train_size
first observations. If refit
is True, the model is trained in each iteration
increasing the training set. A copy of the original forecaster is created so
it is not modified during the process.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate)

Forecaster model. 
required 
series 
pandas DataFrame

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`

allow_incomplete_fold 
bool

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

`True`

levels 
(str, list)

Time series to be predicted. If 
`None`

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. If 
`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`

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 

metrics_levels 
pandas DataFrame

Value(s) of the metric(s). Index are the levels and columns the metrics. 
backtest_predictions 
pandas DataFrame

Value of predictions and their estimated interval if

Source code in skforecast\model_selection_multiseries\model_selection_multiseries.py
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grid_search_forecaster_multivariate(forecaster, series, param_grid, steps, metric, initial_train_size, fixed_train_size=True, gap=0, allow_incomplete_fold=True, levels=None, exog=None, lags_grid=None, refit=False, return_best=True, n_jobs='auto', verbose=True, show_progress=True)
¶
This function is an alias of grid_search_forecaster_multiseries.
Exhaustive search over specified parameter values for a Forecaster object. Validation is done using multiseries backtesting.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate)

Forecaster model. 
required 
series 
pandas DataFrame

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`

allow_incomplete_fold 
bool

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

`True`

levels 
(str, list)

level ( 
`None`

exog 
pandas Series, pandas DataFrame

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

lags_grid 
list of int, lists, np.narray or range

Lists of 
`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

Returns:
Name  Type  Description 

results 
pandas DataFrame

Results for each combination of parameters.

Source code in skforecast\model_selection_multiseries\model_selection_multiseries.py
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random_search_forecaster_multivariate(forecaster, series, param_distributions, steps, metric, initial_train_size, fixed_train_size=True, gap=0, allow_incomplete_fold=True, levels=None, exog=None, lags_grid=None, refit=False, n_iter=10, random_state=123, return_best=True, n_jobs='auto', verbose=True, show_progress=True)
¶
This function is an alias of random_search_forecaster_multiseries.
Random search over specified parameter values or distributions for a Forecaster object. Validation is done using multiseries backtesting.
Parameters:
Name  Type  Description  Default 

forecaster 
(ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate)

Forecaster model. 
required 
series 
pandas DataFrame

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`

allow_incomplete_fold 
bool

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

`True`

levels 
(str, list)

level ( 
`None`

exog 
pandas Series, pandas DataFrame

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

lags_grid 
list of int, lists, np.narray or range

Lists of 
`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

Returns:
Name  Type  Description 

results 
pandas DataFrame

Results for each combination of parameters.

Source code in skforecast\model_selection_multiseries\model_selection_multiseries.py
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