orbit.models package

Submodules

orbit.models.base_model module

orbit.models.dlt module

class orbit.models.dlt.BaseDLT(regressor_col=None, regressor_sign=None, regressor_beta_prior=None, regressor_sigma_prior=None, regression_penalty='fixed_ridge', lasso_scale=0.5, auto_ridge_scale=0.5, slope_sm_input=None, period=1, damped_factor=0.8, global_trend_option='linear', **kwargs)

Bases: orbit.models.ets.BaseETS

Base DLT model object with shared functionality for Full, Aggregated, and MAP methods

The model arguments are the same as BaseLGT with some additional arguments

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

  • period (int) – Used to set time_delta as 1 / max(period, seasonality). If None and no seasonality, then time_delta == 1

  • damped_factor (float) – Hyperparameter float value between [0, 1]. A smaller value further dampens the previous global trend value. Default, 0.8

  • global_trend_option ({ 'flat', 'linear', 'loglinear', 'logistic' }) – Transformation function for the shape of the forecasted global trend.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

class orbit.models.dlt.DLTAggregated(**kwargs)

Bases: orbit.models.ets.ETSAggregated, orbit.models.dlt.BaseDLT

Concrete DLT model for aggregated posterior prediction

The model arguments are the same as ETSAggregated with some additional arguments

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

  • period (int) – Used to set time_delta as 1 / max(period, seasonality). If None and no seasonality, then time_delta == 1

  • damped_factor (float) – Hyperparameter float value between [0, 1]. A smaller value further dampens the previous global trend value. Default, 0.8

  • global_trend_option ({ 'flat', 'linear', 'loglinear', 'logistic' }) – Transformation function for the shape of the forecasted global trend.

  • aggregate_method ({ 'mean', 'median' }) – Method used to reduce parameter posterior samples

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • aggregate_method – Method used to reduce parameter posterior samples

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

See also

class

~orbit.model.ets.BaseETS

orbit.models.ets.ETSAggregated

get_regression_coefs()
class orbit.models.dlt.DLTFull(**kwargs)

Bases: orbit.models.ets.ETSFull, orbit.models.dlt.BaseDLT

Concrete DLT model for full prediction

The model arguments are the same as ETSFull with some additional arguments

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

  • period (int) – Used to set time_delta as 1 / max(period, seasonality). If None and no seasonality, then time_delta == 1

  • damped_factor (float) – Hyperparameter float value between [0, 1]. A smaller value further dampens the previous global trend value. Default, 0.8

  • global_trend_option ({ 'flat', 'linear', 'loglinear', 'logistic' }) – Transformation function for the shape of the forecasted global trend.

  • n_bootstrap_draws (int) – Number of bootstrap samples to draw from the initial MCMC or VI posterior samples. If None, use the original posterior draws.

  • prediction_percentiles (list) – List of integers of prediction percentiles that should be returned on prediction. To avoid reporting any confident intervals, pass an empty list

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • n_bootstrap_draws – Number of bootstrap samples to draw from the initial MCMC or VI posterior samples. If None, use the original posterior draws.

  • prediction_percentiles – List of integers of prediction percentiles that should be returned on prediction. To avoid reporting any confident intervals, pass an empty list

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

See also

class

~orbit.model.ets.BaseETS

orbit.models.ets.ETSFull

get_regression_coefs(aggregate_method='mean')
class orbit.models.dlt.DLTMAP(**kwargs)

Bases: orbit.models.ets.ETSMAP, orbit.models.dlt.BaseDLT

Concrete DLT model for MAP (Maximum a Posteriori) prediction

The model arguments are the same as ETSMAP with some additional arguments

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

  • period (int) – Used to set time_delta as 1 / max(period, seasonality). If None and no seasonality, then time_delta == 1

  • damped_factor (float) – Hyperparameter float value between [0, 1]. A smaller value further dampens the previous global trend value. Default, 0.8

  • global_trend_option ({ 'flat', 'linear', 'loglinear', 'logistic' }) – Transformation function for the shape of the forecasted global trend.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

See also

class

~orbit.model.ets.BaseETS

orbit.models.ets.ETSMAP

get_regression_coefs()

orbit.models.lgt module

class orbit.models.lgt.BaseLGT(regressor_col=None, regressor_sign=None, regressor_beta_prior=None, regressor_sigma_prior=None, regression_penalty='fixed_ridge', lasso_scale=0.5, auto_ridge_scale=0.5, slope_sm_input=None, **kwargs)

Bases: orbit.models.ets.BaseETS

Base LGT model object with shared functionality for Full, Aggregated, and MAP methods

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

class orbit.models.lgt.LGTAggregated(**kwargs)

Bases: orbit.models.lgt.BaseLGT, orbit.models.ets.ETSAggregated

Concrete LGT model for aggregated posterior prediction

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • aggregate_method ({ 'mean', 'median' }) – Method used to reduce parameter posterior samples

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • aggregate_method – Method used to reduce parameter posterior samples

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

get_regression_coefs()
class orbit.models.lgt.LGTFull(**kwargs)

Bases: orbit.models.lgt.BaseLGT, orbit.models.ets.ETSFull

Concrete LGT model for full Bayesian prediction

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • n_bootstrap_draws (int) – Number of bootstrap samples to draw from the initial MCMC or VI posterior samples. If None, use the original posterior draws.

  • prediction_percentiles (list) – List of integers of prediction percentiles that should be returned on prediction. To avoid reporting any confident intervals, pass an empty list

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • n_bootstrap_draws – Number of bootstrap samples to draw from the initial MCMC or VI posterior samples. If None, use the original posterior draws.

  • prediction_percentiles – List of integers of prediction percentiles that should be returned on prediction. To avoid reporting any confident intervals, pass an empty list

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • seasonality – Length of seasonality

  • seasonality_sm_input – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

get_regression_coefs(aggregate_method='mean')
class orbit.models.lgt.LGTMAP(regressor_col=None, regressor_sign=None, regressor_beta_prior=None, regressor_sigma_prior=None, regression_penalty='fixed_ridge', lasso_scale=0.5, auto_ridge_scale=0.5, slope_sm_input=None, **kwargs)

Bases: orbit.models.lgt.BaseLGT, orbit.models.ets.ETSMAP

Concrete LGT model for MAP (Maximum a Posteriori) prediction

Similar to :class: ~orbit.models.LGTAggregated but prediction is based on Maximum a Posteriori (aka Mode) of the posterior.

This model only supports MAP estimating `estimator_type`s

Parameters

  • response_col (str) – Name of response variable column, default ‘y’

  • date_col (str) – Name of date variable column, default ‘ds’

  • estimator_type (orbit.BaseEstimator) – Any subclass of orbit.BaseEstimator

  • seasonality (int) – Length of seasonality

  • seasonality_sm_input (float) – float value between [0, 1], applicable only if seasonality > 1. A larger value puts more weight on the current seasonality. If None, the model will estimate this value.

  • level_sm_input (float) – float value between [0.0001, 1]. A larger value puts more weight on the current level. If None, the model will estimate this value.

  • regressor_col (list) – Names of regressor columns, if any

  • regressor_sign (list) – list with values { ‘+’, ‘-‘, ‘=’ } such that ‘+’ indicates regressor coefficient estimates are constrained to [0, inf). ‘-‘ indicates regressor coefficient estimates are constrained to (-inf, 0]. ‘=’ indicates regressor coefficient estimates can be any value between (-inf, inf). The length of regressor_sign must be the same length as regressor_col. If None, all elements of list will be set to ‘=’.

  • regressor_beta_prior (list) – list of prior float values for regressor coefficient betas. The length of regressor_beta_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regressor_sigma_prior (list) – list of prior float values for regressor coefficient sigmas. The length of regressor_sigma_prior must be the same length as regressor_col. If None, use non-informative priors.

  • regression_penalty ({ 'fixed_ridge', 'lasso', 'auto_ridge' }) – regression penalty method

  • lasso_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘lasso’

  • auto_ridge_scale (float) – float value between [0, 1], applicable only if regression_penalty == ‘auto_ridge’

  • slope_sm_input (float) – float value between [0, 1]. A larger value puts more weight on the current slope. If None, the model will estimate this value.

Other Parameters

**kwargs (additional arguments passed into orbit.estimators.stan_estimator or orbit.estimators.pyro_estimator)

get_regression_coefs()

Module contents