pmdarima.pipeline
.Pipeline¶

class
pmdarima.pipeline.
Pipeline
(steps)[source][source]¶ A pipeline of transformers with an optional final estimator stage
The pipeline object chains together an arbitrary number of named, ordered transformations, passing the output from one as the input to the next. As the last stage, an
ARIMA
orAutoARIMA
object will be fit. This pipeline takes after the scikitlearnsklearn.Pipeline
object, which behaves similarly but does not share the same timeseries interface thatpmdarima
follows.The purpose of the pipeline is to assemble several steps that can be crossvalidated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a ‘__’, as in the example below.
Parameters: steps : list
List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an ARIMA or AutoARIMA estimator.
Attributes
named_steps
Map the steps to a dictionary Examples
>>> from pmdarima.datasets import load_wineind >>> from pmdarima.arima import AutoARIMA >>> from pmdarima.pipeline import Pipeline >>> from pmdarima.preprocessing import FourierFeaturizer >>> >>> wineind = load_wineind() >>> pipeline = Pipeline([ ... ("fourier", FourierFeaturizer(m=12, k=3)), ... ("arima", AutoARIMA(seasonal=False, stepwise=True, ... suppress_warnings=True, ... error_action='ignore')) ... ]) >>> pipeline.fit(wineind) Pipeline(steps=[('fourier', FourierFeaturizer(k=3, m=12)), ('arima', AutoARIMA(D=None, alpha=0.05, callback=None, d=None, disp=0, error_action='ignore', information_criterion='aic', m=1, max_D=1, max_P=2, max_Q=2, max_d=2, max_order=10, max_p=5, max_q=5, maxiter=None, method=None, n_fits=10, n...s_warnings=True, test='kpss', trace=False, transparams=True, trend=None, with_intercept=True))])
Methods
fit
(y[, X])Fit the pipeline of transformers and the ARIMA model get_params
([deep])Get parameters for this estimator. predict
([n_periods, X, return_conf_int, …])Forecast future (transformed) values predict_in_sample
([X, start, end, dynamic, …])Generate insample predictions from the fit pipeline. set_params
(**params)Set the parameters of this estimator. summary
()Get a summary of the ARIMA model transform
([n_periods, X])Get the transformed X array update
(y[, X, maxiter])Update an ARIMA or autoARIMA as well as any necessary transformers 
fit
(y, X=None, **fit_kwargs)[source][source]¶ Fit the pipeline of transformers and the ARIMA model
Chain the timeseries and X array through a series of transformations, fitting each stage along the way, finally fitting an ARIMA or AutoARIMA model.
Parameters: y : arraylike or iterable, shape=(n_samples,)
The timeseries to which to fit the
ARIMA
estimator. This may either be a PandasSeries
object (statsmodels can internally use the dates in the index), or a numpy array. This should be a onedimensional array of floats, and should not contain anynp.nan
ornp.inf
values.X : arraylike, shape=[n_obs, n_vars], optional (default=None)
An optional 2d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an
ARIMA
is fit on exogenous features, it must be provided exogenous features for making predictions.**fit_kwargs : keyword args
Extra keyword arguments used for each stage’s
fit
stage. Similar to scikitlearn pipeline keyword args, the keys are compound, comprised of the stage name and the argument name separated by a “__”. For instance, if fitting an ARIMA in stage “arima”, your kwargs may resemble:{"arima__maxiter": 10}

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.

named_steps
¶ Map the steps to a dictionary

predict
(n_periods=10, X=None, return_conf_int=False, alpha=0.05, inverse_transform=True, **kwargs)[source][source]¶ Forecast future (transformed) values
Generate predictions (forecasts)
n_periods
in the future. Note that if anX
array was used in the model fit, it will be expected for the predict procedure and will fail otherwise. Forecasts may be transformed by the endogenous steps along the way and might be on a different scale than raw training/test data.Parameters: n_periods : int, optional (default=10)
The number of periods in the future to forecast.
X : arraylike, shape=[n_obs, n_vars], optional (default=None)
An optional 2d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an
ARIMA
is fit on exogenous features, it must be provided exogenous features for making predictions.return_conf_int : bool, optional (default=False)
Whether to get the confidence intervals of the forecasts.
alpha : float, optional (default=0.05)
The confidence intervals for the forecasts are (1  alpha) %
inverse_transform : bool, optional (default=True)
Whether to inverse transform predictions, if they are in log or BoxCox scale. Any endog transformer will be inversetransformed.
**kwargs : keyword args
Extra keyword arguments used for each stage’s
transform
stage and the estimator’spredict
stage. Similar to scikitlearn pipeline keyword args, the keys are compound, comprised of the stage name and the argument name separated by a “__”. For instance, if you have a FourierFeaturizer whose stage is named “fourier”, your transform kwargs could resemble:{"fourier__n_periods": 50}
Returns: forecasts : arraylike, shape=(n_periods,)
The array of transformed, forecasted values.
conf_int : arraylike, shape=(n_periods, 2), optional
The confidence intervals for the forecasts. Only returned if
return_conf_int
is True.

predict_in_sample
(X=None, start=None, end=None, dynamic=False, return_conf_int=False, alpha=0.05, inverse_transform=True, **kwargs)[source][source]¶ Generate insample predictions from the fit pipeline.
Predicts the original training (insample) time series values. This can be useful when wanting to visualize the fit, and qualitatively inspect the efficacy of the model, or when wanting to compute the residuals of the model.
Parameters: X : arraylike, shape=[n_obs, n_vars], optional (default=None)
An optional 2d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an
ARIMA
is fit on exogenous features, it must be provided exogenous features for making predictions.start : int, optional (default=None)
Zeroindexed observation number at which to start forecasting, ie., the first forecast is start.
end : int, optional (default=None)
Zeroindexed observation number at which to end forecasting, ie., the first forecast is start.
dynamic : bool, optional (default=False)
The dynamic keyword affects insample prediction. If dynamic is False, then the insample lagged values are used for prediction. If dynamic is True, then insample forecasts are used in place of lagged dependent variables. The first forecasted value is start.
return_conf_int : bool, optional (default=False)
Whether to get the confidence intervals of the forecasts.
alpha : float, optional (default=0.05)
The confidence intervals for the forecasts are (1  alpha) %
inverse_transform : bool, optional (default=True)
Whether to inverse transform predictions, if they are in log or BoxCox scale. Any endog transformer will be inversetransformed.
**kwargs : keyword args
Extra keyword arguments used for each stage’s
transform
stage. Similar to scikitlearn pipeline keyword args, the keys are compound, comprised of the stage name and the argument name separated by a “__”. For instance, if you have a FourierFeaturizer whose stage is named “fourier”, your transform kwargs could resemble:{"fourier__n_periods": 50}
Returns: preds : array
The predicted values.
conf_int : arraylike, shape=(n_periods, 2), optional
The confidence intervals for the predictions. Only returned if
return_conf_int
is True.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Parameters: **params : dict
Estimator parameters.
Returns: self : object
Estimator instance.

transform
(n_periods=10, X=None, **kwargs)[source][source]¶ Get the transformed X array
Generate the X array
n_periods
in the future. This passes the provided exog variables through all transformation steps, returning it before it’s passed into the model.Parameters: n_periods : int, optional (default=10)
The number of periods in the future to forecast.
X : arraylike, shape=[n_obs, n_vars], optional (default=None)
An optional 2d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an
ARIMA
is fit on exogenous features, it must be provided exogenous features for making predictions.**kwargs : keyword args
Extra keyword arguments used for each stage’s
transform
stage and the estimator’spredict
stage. Similar to scikitlearn pipeline keyword args, the keys are compound, comprised of the stage name and the argument name separated by a “__”. For instance, if you have a FourierFeaturizer whose stage is named “fourier”, your transform kwargs could resemble:{"fourier__n_periods": 50}
Returns: X_prime : pd.DataFrame
The transformed exog array.

update
(y, X=None, maxiter=None, **kwargs)[source][source]¶ Update an ARIMA or autoARIMA as well as any necessary transformers
Passes the newly observed values through the appropriate endog transformations, and the
X
array through the exog transformers (updating where necessary) before finally updating the ARIMA model.Parameters: y : arraylike or iterable, shape=(n_samples,)
The timeseries data to add to the endogenous samples on which the
ARIMA
estimator was previously fit. This may either be a PandasSeries
object or a numpy array. This should be a one dimensional array of finite floats.X : arraylike, shape=[n_obs, n_vars], optional (default=None)
An optional 2d array of exogenous variables. If the model was fit with an exogenous array of covariates, it will be required for updating the observed values.
maxiter : int, optional (default=None)
The number of iterations to perform when updating the model. If None, will perform
max(5, n_samples // 10)
iterations.**kwargs : keyword args
Extra keyword arguments used for each stage’s
update
stage. Similar to scikitlearn pipeline keyword args, the keys are compound, comprised of the stage name and the argument name separated by a “__”.
