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module neuralforecast.models.timemixer

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class DataEmbedding_wo_pos

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method __init__

__init__(c_in, d_model, dropout=0.1, embed_type='fixed', freq='h')

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method forward

forward(x, x_mark)

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class DFT_series_decomp

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method __init__

__init__(top_k)

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method forward

forward(x)

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class MultiScaleSeasonMixing

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method __init__

__init__(seq_len, down_sampling_window, down_sampling_layers)

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method forward

forward(season_list)

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class MultiScaleTrendMixing

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method __init__

__init__(seq_len, down_sampling_window, down_sampling_layers)

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method forward

forward(trend_list)

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class PastDecomposableMixing

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method __init__

__init__(
    seq_len,
    pred_len,
    down_sampling_window,
    down_sampling_layers,
    d_model,
    dropout,
    channel_independence,
    decomp_method,
    d_ff,
    moving_avg,
    top_k
)

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method forward

forward(x_list)

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class TimeMixer

• 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.
• stat_exog_list (list): static exogenous columns.
• hist_exog_list (list): historic exogenous columns.
• futr_exog_list (list): future exogenous columns.
• d_model (int): dimension of the model.
• d_ff (int): dimension of the fully-connected network.
• dropout (float): dropout rate.
• e_layers (int): number of encoder layers.
• top_k (int): number of selected frequencies.
• decomp_method (str): method of series decomposition [moving_avg, dft_decomp].
• moving_avg (int): window size of moving average.
• channel_independence (int): 0: channel dependence, 1: channel independence.
• down_sampling_layers (int): number of downsampling layers.
• down_sampling_window (int): size of downsampling window.
• down_sampling_method (str): down sampling method [avg, max, conv].
• use_norm (bool): whether to normalize or not.
• decoder_input_size_multiplier (float): 0.5.
• 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 (keyword): trainer arguments inherited from PyTorch Lighning’s trainer.

• Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou.”TimeMixer: Decomposable Multiscale Mixing For Time Series Forecasting”

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method __init__

__init__(
    h,
    input_size,
    n_series,
    stat_exog_list=None,
    hist_exog_list=None,
    futr_exog_list=None,
    d_model: int = 32,
    d_ff: int = 32,
    dropout: float = 0.1,
    e_layers: int = 4,
    top_k: int = 5,
    decomp_method: str = 'moving_avg',
    moving_avg: int = 25,
    channel_independence: int = 0,
    down_sampling_layers: int = 1,
    down_sampling_window: int = 2,
    down_sampling_method: str = 'avg',
    use_norm: bool = True,
    decoder_input_size_multiplier: float = 0.5,
    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 forecast

forecast(x_enc, x_mark_enc, x_mark_dec)

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method forward

forward(windows_batch)

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method future_multi_mixing

future_multi_mixing(B, enc_out_list, x_list)

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method out_projection

out_projection(dec_out, i, out_res)

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method pre_enc

pre_enc(x_list)