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module neuralforecast.models.timesnet

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function FFT_for_Period

FFT_for_Period(x, k=2)

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class Inception_Block_V1

Inception_Block_V1

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method __init__

__init__(in_channels, out_channels, num_kernels=6, init_weight=True)

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method forward

forward(x)

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class TimesBlock

TimesBlock

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method __init__

__init__(input_size, h, k, hidden_size, conv_hidden_size, num_kernels)

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method forward

forward(x)

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class TimesNet

TimesNet The TimesNet univariate model tackles the challenge of modeling multiple intraperiod and interperiod temporal variations. Args:
  • h (int): Forecast horizon.
  • input_size (int): Length of input window (lags).
  • stat_exog_list (list of str): optional (default=None), Static exogenous columns.
  • hist_exog_list (list of str): optional (default=None), Historic exogenous columns.
  • futr_exog_list (list of str): optional (default=None), Future exogenous columns.
  • exclude_insample_y (bool): The model skips the autoregressive features y[t-input_size:t] if True.
  • hidden_size (int): Size of embedding for embedding and encoders.
  • dropout (float): Dropout for embeddings.
  • conv_hidden_size (int): Channels of the Inception block.
  • top_k (int): Number of periods.
  • num_kernels (int): Number of kernels for the Inception block.
  • encoder_layers (int): Number of encoder layers.
  • loss (PyTorch module): Instantiated train loss class from losses collection.
  • valid_loss (PyTorch module): Instantiated validation loss class from losses collection.
  • max_steps (int): Maximum number of training steps.
  • learning_rate (float): Learning rate.
  • num_lr_decays (int): Number of learning rate decays, evenly distributed across max_steps. If -1, no learning rate decay is performed.
  • early_stop_patience_steps (int): Number of validation iterations before early stopping. If -1, no early stopping is performed.
  • 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.
  • inference_windows_batch_size (int): Number of windows to sample in each inference batch.
  • 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 (default=None), Custom name of the model.
  • optimizer (Subclass of ‘torch.optim.Optimizer’): optional (default=None), User specified optimizer instead of the default choice (Adam).
  • optimizer_kwargs (dict): optional (defualt=None), 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 (default=None), 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: int,
    input_size: int,
    stat_exog_list=None,
    hist_exog_list=None,
    futr_exog_list=None,
    exclude_insample_y=False,
    hidden_size: int = 64,
    dropout: float = 0.1,
    conv_hidden_size: int = 64,
    top_k: int = 5,
    num_kernels: int = 6,
    encoder_layers: int = 2,
    loss=MAE(),
    valid_loss=None,
    max_steps: int = 1000,
    learning_rate: float = 0.0001,
    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=64,
    inference_windows_batch_size=256,
    start_padding_enabled=False,
    training_data_availability_threshold=0.0,
    step_size: int = 1,
    scaler_type: str = 'standard',
    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

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 forward

forward(windows_batch)