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module neuralforecast.models.rmok

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class WaveKANLayer

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method __init__

__init__(
    in_features,
    out_features,
    wavelet_type='mexican_hat',
    with_bn=True,
    device='cpu'
)

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method forward

forward(x)

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method wavelet_transform

wavelet_transform(x)

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class TaylorKANLayer

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method __init__

__init__(input_dim, out_dim, order, addbias=True)

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method forward

forward(x)

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class JacobiKANLayer

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method __init__

__init__(input_dim, output_dim, degree, a=1.0, b=1.0)

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method forward

forward(x)

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class RMoK

• h (int): forecast horizon.
• input_size (int): autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].
• n_series (int): number of time-series.
• futr_exog_list (str list): future exogenous columns.
• hist_exog_list (str list): historic exogenous columns.
• stat_exog_list (str list): static exogenous columns.
• taylor_order (int): order of the Taylor polynomial.
• jacobi_degree (int): degree of the Jacobi polynomial.
• wavelet_function (str): wavelet function to use in the WaveKAN. Choose from [“mexican_hat”, “morlet”, “dog”, “meyer”, “shannon”]
• dropout (float): dropout rate.
• revin_affine (bool): bool to use affine in RevIn.
• loss (PyTorch module): instantiated train loss class from losses collection.
• valid_loss (PyTorch module): instantiated valid loss class from losses collection.
• max_steps (int): maximum number of training steps.
• learning_rate (float): learning rate between (0, 1).
• num_lr_decays (int): number of learning rate decays, evenly distributed across max_steps.
• early_stop_patience_steps (int): number of validation iterations before early stopping.
• val_check_steps (int): number of training steps between every validation loss check.
• batch_size (int): number of different series in each batch.
• valid_batch_size (int): number of different series in each validation and test batch, if None uses batch_size.
• windows_batch_size (int): number of windows to sample in each training batch, default uses all.
• inference_windows_batch_size (int): number of windows to sample in each inference batch, -1 uses all.
• start_padding_enabled (bool): if True, the model will pad the time series with zeros at the beginning, by input size.
• training_data_availability_threshold (Union[float, List[float]]): minimum fraction of valid data points required for training windows. Single float applies to both insample and outsample; list of two floats specifies [insample_fraction, outsample_fraction]. Default 0.0 allows windows with only 1 valid data point (current behavior).
• step_size (int): step size between each window of temporal data.
• scaler_type (str): type of scaler for temporal inputs normalization see temporal scalers.
• random_seed (int): random_seed for pytorch initializer and numpy generators.
• drop_last_loader (bool): if True TimeSeriesDataLoader drops last non-full batch.
• alias (str): optional, Custom name of the model.
• optimizer (Subclass of ‘torch.optim.Optimizer’): optional, user specified optimizer instead of the default choice (Adam).
• optimizer_kwargs (dict): optional, list of parameters used by the user specified optimizer.
• lr_scheduler (Subclass of ‘torch.optim.lr_scheduler.LRScheduler’): optional, user specified lr_scheduler instead of the default choice (StepLR).
• lr_scheduler_kwargs (dict): optional, list of parameters used by the user specified lr_scheduler.
• dataloader_kwargs (dict): optional, list of parameters passed into the PyTorch Lightning dataloader by the TimeSeriesDataLoader.
• **trainer_kwargs (int): keyword trainer arguments inherited from PyTorch Lighning’s trainer.

• Xiao Han, Xinfeng Zhang, Yiling Wu, Zhenduo Zhang, Zhe Wu.”KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?”. arXiv.

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method __init__

__init__(
    h,
    input_size,
    n_series: int,
    futr_exog_list=None,
    hist_exog_list=None,
    stat_exog_list=None,
    taylor_order: int = 3,
    jacobi_degree: int = 6,
    wavelet_function: str = 'mexican_hat',
    dropout: float = 0.1,
    revin_affine: bool = True,
    loss=MAE(),
    valid_loss=None,
    max_steps: int = 1000,
    learning_rate: float = 0.001,
    num_lr_decays: int = -1,
    early_stop_patience_steps: int = -1,
    val_check_steps: int = 100,
    batch_size: int = 32,
    valid_batch_size: Optional[int] = None,
    windows_batch_size=32,
    inference_windows_batch_size=32,
    start_padding_enabled=False,
    training_data_availability_threshold=0.0,
    step_size: int = 1,
    scaler_type: str = 'identity',
    random_seed: int = 1,
    drop_last_loader: bool = False,
    alias: Optional[str] = None,
    optimizer=None,
    optimizer_kwargs=None,
    lr_scheduler=None,
    lr_scheduler_kwargs=None,
    dataloader_kwargs=None,
    **trainer_kwargs
)

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property automatic_optimization

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property current_epoch

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property device

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property device_mesh

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property dtype

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property example_input_array

• Single tensor: It is assumed the model takes a single argument, i.e., model.forward(model.example_input_array)
• Tuple: The input array should be interpreted as a sequence of positional arguments, i.e., model.forward(*model.example_input_array)
• Dict: The input array represents named keyword arguments, i.e., model.forward(**model.example_input_array)

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property fabric

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property global_rank

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property global_step

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property hparams

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property hparams_initial

• AttributeDict: immutable initial hyperparameters

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property local_rank

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property logger

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property loggers

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property on_gpu

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property strict_loading

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property trainer

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method forward

forward(windows_batch)