Core Module Documentation#

class neuralprophet.configure.AR(n_lags: 'int', ar_reg: 'Optional[float]' = None)#
regularize(weights, original=False)#

Regularization of AR coefficients

Parameters:
  • weights (torch.Tensor) – Model weights to be regularized towards zero

  • original (bool) – Do not penalize non-zeros

Returns:

Regularization loss

Return type:

numeric

class neuralprophet.configure.AllSeason(mode: 'str' = 'additive', computation: 'str' = 'fourier', reg_lambda: 'float' = 0, yearly_arg: 'Union[str, bool, int]' = 'auto', weekly_arg: 'Union[str, bool, int]' = 'auto', daily_arg: 'Union[str, bool, int]' = 'auto', global_local: 'str' = 'local')#
neuralprophet.configure.ConfigCountryHolidays#

alias of Holidays

class neuralprophet.configure.Event(lower_window: 'int', upper_window: 'int', reg_lambda: 'float', mode: 'str')#
class neuralprophet.configure.Holidays(country: 'str', lower_window: 'int', upper_window: 'int', mode: 'str' = 'additive', reg_lambda: 'float' = None)#
class neuralprophet.configure.LaggedRegressor(reg_lambda: 'Optional[float]', as_scalar: 'bool', normalize: 'Union[bool, str]', n_lags: 'int')#
class neuralprophet.configure.MissingDataHandling(impute_missing: 'bool' = True, impute_linear: 'int' = 10, impute_rolling: 'int' = 10, drop_missing: 'bool' = False)#
class neuralprophet.configure.Model(num_hidden_layers: 'int', d_hidden: 'int')#
class neuralprophet.configure.Normalization(normalize: 'str', global_normalization: 'bool', global_time_normalization: 'bool', unknown_data_normalization: 'bool', local_data_params: 'dict' = None, global_data_params: 'dict' = None)#
class neuralprophet.configure.Regressor(reg_lambda: 'float', normalize: 'str', mode: 'str')#
class neuralprophet.configure.Season(resolution: 'int', period: 'float', arg: 'str')#
class neuralprophet.configure.Train(quantiles: 'Union[list, None]', learning_rate: 'Union[float, None]', epochs: 'Union[int, None]', batch_size: 'Union[int, None]', loss_func: 'Union[str, torch.nn.modules.loss._Loss, Callable]', optimizer: 'Union[str, torch.optim.Optimizer]', newer_samples_weight: 'float' = 1.0, newer_samples_start: 'float' = 0.0, reg_delay_pct: 'float' = 0.5, reg_lambda_trend: 'float' = None, trend_reg_threshold: 'Union[bool, float]' = None, reg_lambda_season: 'float' = None)#
class neuralprophet.configure.Trend(growth: 'str', changepoints: 'list', n_changepoints: 'int', changepoints_range: 'float', trend_reg: 'float', trend_reg_threshold: 'Union[bool, float]', trend_global_local: 'str')#