Core Module Documentation

class neuralprophet.configure.AR(n_lags: int, ar_sparsity: float)
regularize(weights, original=False)

Regularization of AR coefficients :param weights: Model weights to be regularized towards zero :type weights: torch tensor

Returns

regularization loss, scalar

class neuralprophet.configure.AllSeason(mode: str = 'additive', computation: str = 'fourier', reg_lambda: float = 0, yearly_arg: (<class 'str'>, <class 'bool'>, <class 'int'>) = 'auto', weekly_arg: (<class 'str'>, <class 'bool'>, <class 'int'>) = 'auto', daily_arg: (<class 'str'>, <class 'bool'>, <class 'int'>) = 'auto')
class neuralprophet.configure.Covar(reg_lambda: float, as_scalar: bool, normalize: (<class 'bool'>, <class 'str'>))
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.Model(num_hidden_layers: int, d_hidden: int)
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(learning_rate: (<class 'float'>, None), epochs: (<class 'int'>, None), batch_size: (<class 'int'>, None), loss_func: (<class 'str'>, <class 'torch.nn.modules.loss._Loss'>, 'typing.Callable'), optimizer: (<class 'str'>, <class 'torch.optim.optimizer.Optimizer'>), train_speed: (<class 'int'>, <class 'float'>, None), ar_sparsity: (<class 'float'>, None), reg_delay_pct: float = 0.5, reg_lambda_trend: float = None, trend_reg_threshold: (<class 'bool'>, <class '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: (<class 'bool'>, <class 'float'>))