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module neuralforecast.models.nhits

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Global Variables

  • ACTIVATIONS
  • POOLING

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class NHITSBlock

NHITS block which takes a basis function as an argument.

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method __init__

__init__(
    input_size: int,
    h: int,
    n_theta: int,
    mlp_units: list,
    basis: Module,
    futr_input_size: int,
    hist_input_size: int,
    stat_input_size: int,
    n_pool_kernel_size: int,
    pooling_mode: str,
    dropout_prob: float,
    activation: str
)

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method forward

forward(
    insample_y: Tensor,
    futr_exog: Tensor,
    hist_exog: Tensor,
    stat_exog: Tensor
) → Tuple[Tensor, Tensor]

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class NHITS

NHITS The Neural Hierarchical Interpolation for Time Series (NHITS), is an MLP-based deep neural architecture with backward and forward residual links. NHITS tackles volatility and memory complexity challenges, by locally specializing its sequential predictions into the signals frequencies with hierarchical interpolation and pooling. Args:
  • h (int): Forecast horizon.
  • input_size (int): autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].
  • futr_exog_list (str list): future exogenous columns.
  • hist_exog_list (str list): historic exogenous columns.
  • stat_exog_list (str list): static exogenous columns.
  • exclude_insample_y (bool): the model skips the autoregressive features y[t-input_size:t] if True.
  • stack_types (List[str]): stacks list in the form N * [‘identity’], to be deprecated in favor of n_stacks. Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
  • n_blocks (List[int]): Number of blocks for each stack. Note that len(n_blocks) = len(stack_types).
  • mlp_units (List[List[int]]): Structure of hidden layers for each stack type. Each internal list should contain the number of units of each hidden layer. Note that len(n_hidden) = len(stack_types).
  • n_pool_kernel_size (List[int]): list with the size of the windows to take a max/avg over. Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
  • n_freq_downsample (List[int]): list with the stack’s coefficients (inverse expressivity ratios). Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
  • pooling_mode (str): input pooling module from [‘MaxPool1d’, ‘AvgPool1d’].
  • interpolation_mode (str): interpolation basis from [‘linear’, ‘nearest’, ‘cubic’].
  • dropout_prob_theta (float): Float between (0, 1). Dropout for NHITS basis.
  • activation (str): activation from [‘ReLU’, ‘Softplus’, ‘Tanh’, ‘SELU’, ‘LeakyReLU’, ‘PReLU’, ‘Sigmoid’].
  • 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.
References:

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method __init__

__init__(
    h,
    input_size,
    futr_exog_list=None,
    hist_exog_list=None,
    stat_exog_list=None,
    exclude_insample_y=False,
    stack_types: list = ['identity', 'identity', 'identity'],
    n_blocks: list = [1, 1, 1],
    mlp_units: list = [[512, 512], [512, 512], [512, 512]],
    n_pool_kernel_size: list = [2, 2, 1],
    n_freq_downsample: list = [4, 2, 1],
    pooling_mode: str = 'MaxPool1d',
    interpolation_mode: str = 'linear',
    dropout_prob_theta=0.0,
    activation='ReLU',
    loss=MAE(),
    valid_loss=None,
    max_steps: int = 1000,
    learning_rate: float = 0.001,
    num_lr_decays: int = 3,
    early_stop_patience_steps: int = -1,
    val_check_steps: int = 100,
    batch_size: int = 32,
    valid_batch_size: Optional[int] = None,
    windows_batch_size: int = 1024,
    inference_windows_batch_size: int = -1,
    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=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

If set to False you are responsible for calling .backward(), .step(), .zero_grad().

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property current_epoch

The current epoch in the Trainer, or 0 if not attached.

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property device

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property device_mesh

Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the :meth:~pytorch_lightning.core.hooks.ModelHooks.configure_model hook to parallelize the LightningModule.

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property dtype

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property example_input_array

The example input array is a specification of what the module can consume in the :meth:forward method. The return type is interpreted as follows:
  • 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

The index of the current process across all nodes and devices.

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property global_step

Total training batches seen across all epochs. If no Trainer is attached, this property is 0.

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property hparams

The collection of hyperparameters saved with :meth:save_hyperparameters. It is mutable by the user. For the frozen set of initial hyperparameters, use :attr:hparams_initial. Returns: Mutable hyperparameters dictionary

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property hparams_initial

The collection of hyperparameters saved with :meth:save_hyperparameters. These contents are read-only. Manual updates to the saved hyperparameters can instead be performed through :attr:hparams. Returns:
  • AttributeDict: immutable initial hyperparameters

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property local_rank

The index of the current process within a single node.

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property logger

Reference to the logger object in the Trainer.

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property loggers

Reference to the list of loggers in the Trainer.

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property on_gpu

Returns True if this model is currently located on a GPU. Useful to set flags around the LightningModule for different CPU vs GPU behavior.

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property strict_loading

Determines how Lightning loads this model using .load_state_dict(..., strict=model.strict_loading).

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property trainer

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method create_stack

create_stack(
    h,
    input_size,
    stack_types,
    n_blocks,
    mlp_units,
    n_pool_kernel_size,
    n_freq_downsample,
    pooling_mode,
    interpolation_mode,
    dropout_prob_theta,
    activation,
    futr_input_size,
    hist_input_size,
    stat_input_size
)

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method forward

forward(windows_batch)