#
PatchTSMixer

##
Overview

The PatchTSMixer model was proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam.

PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. In this HuggingFace implementation, we provide PatchTSMixer’s capabilities to effortlessly facilitate lightweight mixing across patches, channels, and hidden features for effective multivariate time-series modeling. It also supports various attention mechanisms starting from simple gated attention to more complex self-attention blocks that can be customized accordingly. The model can be pretrained and subsequently used for various downstream tasks such as forecasting, classification and regression.

The abstract from the paper is the following:

TSMixer is a lightweight neural architecture exclusively composed of multi-layer perceptron (MLP) modules designed for multivariate forecasting and representation learning on patched time series. Our model draws inspiration from the success of MLP-Mixer models in computer vision. We demonstrate the challenges involved in adapting Vision MLP-Mixer for time series and introduce empirically validated components to enhance accuracy. This includes a novel design paradigm of attaching online reconciliation heads to the MLP-Mixer backbone, for explicitly modeling the time-series properties such as hierarchy and channel-correlations. We also propose a Hybrid channel modeling approach to effectively handle noisy channel interactions and generalization across diverse datasets, a common challenge in existing patch channel-mixing methods. Additionally, a simple gated attention mechanism is introduced in the backbone to prioritize important features. By incorporating these lightweight components, we significantly enhance the learning capability of simple MLP structures, outperforming complex Transformer models with minimal computing usage. Moreover, TSMixer’s modular design enables compatibility with both supervised and masked self-supervised learning methods, making it a promising building block for time-series Foundation Models. TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%. It also outperforms the latest strong benchmarks of Patch-Transformer models (by 1-2%) with a significant reduction in memory and runtime (2-3X).

This model was contributed by ajati, vijaye12, gsinthong, namctin, wmgifford, kashif.

##
Sample usage

    from transformers import PatchTSMixerConfig, PatchTSMixerForPrediction
    from transformers import Trainer, TrainingArguments,

    config = PatchTSMixerConfig(context_length = 512, prediction_length = 96)
    model = PatchTSMixerForPrediction(config)
    trainer = Trainer(model=model, args=training_args,
                train_dataset=train_dataset,
                eval_dataset=valid_dataset)
    trainer.train()
    results = trainer.evaluate(test_dataset)

##
Usage tips

The model can also be used for time series classification and time series regression. See the respective PatchTSMixerForTimeSeriesClassification and PatchTSMixerForRegression classes.

##
PatchTSMixerConfig

###
class transformers.PatchTSMixerConfig

< source >

( context_length: int = 32 patch_len: int = 8 num_input_channels: int = 1 patch_stride: int = 8 num_parallel_samples: int = 100 d_model: int = 8 expansion_factor: int = 2 num_layers: int = 3 dropout: float = 0.2 mode: str = 'common_channel' gated_attn: bool = True norm_mlp: str = 'LayerNorm' self_attn: bool = False self_attn_heads: int = 1 use_positional_encoding: bool = False positional_encoding_type: str = 'sincos' scaling: typing.Union[str, bool, NoneType] = 'std' loss: str = 'mse' init_std: float = 0.02 post_init: bool = False norm_eps: float = 1e-05 mask_type: str = 'random' random_mask_ratio: float = 0.5 num_forecast_mask_patches: typing.Union[typing.List[int], int, NoneType] = [2] mask_value: int = 0 masked_loss: bool = True channel_consistent_masking: bool = True unmasked_channel_indices: typing.Optional[typing.List[int]] = None head_dropout: float = 0.2 distribution_output: str = 'student_t' prediction_length: int = 16 prediction_channel_indices: list = None num_targets: int = 3 output_range: list = None head_aggregation: str = 'max_pool' **kwargs )

Parameters

- context_length (`int`, optional, defaults to 32) — The context/history length for the input sequence.
- patch_len (`int`, optional, defaults to 8) — The patch length for the input sequence.
- num_input_channels (`int`, optional, defaults to 1) — Number of input variates. For Univariate, set it to 1.
- patch_stride (`int`, optional, defaults to 8) — Determines the overlap between two consecutive patches. Set it to patch_length (or greater), if we want non-overlapping patches.
- num_parallel_samples (`int`, optional, defaults to 100) — The number of samples to generate in parallel for probabilistic forecast.
- d_model (`int`, optional, defaults to 8) — Hidden dimension of the model. Recommended to set it as a multiple of patch_length (i.e. 2-5X of patch_len). Larger value indicates more complex model.
- expansion_factor (`int`, optional, defaults to 2) — Expansion factor to use inside MLP. Recommended range is 2-5. Larger value indicates more complex model.
- num_layers (`int`, optional, defaults to 3) — Number of layers to use. Recommended range is 3-15. Larger value indicates more complex model.
- dropout (`float`, optional, defaults to 0.2) — The dropout probability the `PatchTSMixer` backbone. Recommended range is 0.2-0.7
- mode (`str`, optional, defaults to `"common_channel"`) — Mixer Mode. Determines how to process the channels. Allowed values: “common_channel”, “mix_channel”. In “common_channel” mode, we follow Channel-independent modelling with no explicit channel-mixing. Channel mixing happens in an implicit manner via shared weights across channels. (preferred first approach) In “mix_channel” mode, we follow explicit channel-mixing in addition to patch and feature mixer. (preferred approach when channel correlations are very important to model)
- gated_attn (`bool`, optional, defaults to `True`) — Enable Gated Attention.
- norm_mlp (`str`, optional, defaults to `"LayerNorm"`) — Normalization layer (BatchNorm or LayerNorm).
- self_attn (`bool`, optional, defaults to `False`) — Enable Tiny self attention across patches. This can be enabled when the output of Vanilla PatchTSMixer with gated attention is not satisfactory. Enabling this leads to explicit pair-wise attention and modelling across patches.
- self_attn_heads (`int`, optional, defaults to 1) — Number of self-attention heads. Works only when `self_attn` is set to `True`.
- use_positional_encoding (`bool`, optional, defaults to `False`) — Enable the use of positional embedding for the tiny self-attention layers. Works only when `self_attn` is set to `True`.
- positional_encoding_type (`str`, optional, defaults to `"sincos"`) — Positional encodings. Options `"random"` and `"sincos"` are supported. Works only when `use_positional_encoding` is set to `True`
- scaling (`string` or `bool`, optional, defaults to `"std"`) — Whether to scale the input targets via “mean” scaler, “std” scaler or no scaler if `None`. If `True`, the scaler is set to “mean”.
- loss (`string`, optional, defaults to `"mse"`) — The loss function for the model corresponding to the `distribution_output` head. For parametric distributions it is the negative log likelihood (“nll”) and for point estimates it is the mean squared error “mse”.
- init_std (`float`, optional, defaults to 0.02) — The standard deviation of the truncated normal weight initialization distribution.
- post_init (`bool`, optional, defaults to `False`) — Whether to use custom weight initialization from `transformers` library, or the default initialization in `PyTorch`. Setting it to `False` performs `PyTorch` weight initialization.
- norm_eps (`float`, optional, defaults to 1e-05) — A value added to the denominator for numerical stability of normalization.
- mask_type (`str`, optional, defaults to `"random"`) — Type of masking to use for Masked Pretraining mode. Allowed values are “random”, “forecast”. In Random masking, points are masked randomly. In Forecast masking, points are masked towards the end.
- random_mask_ratio (`float`, optional, defaults to 0.5) — Masking ratio to use when `mask_type` is `random`. Higher value indicates more masking.
- num_forecast_mask_patches (`int` or `list`, optional, defaults to `[2]`) — Number of patches to be masked at the end of each batch sample. If it is an integer, all the samples in the batch will have the same number of masked patches. If it is a list, samples in the batch will be randomly masked by numbers defined in the list. This argument is only used for forecast pretraining.
- mask_value (`float`, optional, defaults to `0.0`) — Mask value to use.
- masked_loss (`bool`, optional, defaults to `True`) — Whether to compute pretraining loss only at the masked portions, or on the entire output.
- channel_consistent_masking (`bool`, optional, defaults to `True`) — When true, masking will be same across all channels of a timeseries. Otherwise, masking positions will vary across channels.
- unmasked_channel_indices (`list`, optional) — Channels that are not masked during pretraining.
- head_dropout (`float`, optional, defaults to 0.2) — The dropout probability the `PatchTSMixer` head.
- distribution_output (`string`, optional, defaults to `"student_t"`) — The distribution emission head for the model when loss is “nll”. Could be either “student_t”, “normal” or “negative_binomial”.
- prediction_length (`int`, optional, defaults to 16) — Number of time steps to forecast for a forecasting task. Also known as the Forecast Horizon.
- prediction_channel_indices (`list`, optional) — List of channel indices to forecast. If None, forecast all channels. Target data is expected to have all channels and we explicitly filter the channels in prediction and target before loss computation.
- num_targets (`int`, optional, defaults to 3) — Number of targets (dimensionality of the regressed variable) for a regression task.
- output_range (`list`, optional) — Output range to restrict for the regression task. Defaults to None.
- head_aggregation (`str`, optional, defaults to `"max_pool"`) — Aggregation mode to enable for classification or regression task. Allowed values are `None`, “use_last”, “max_pool”, “avg_pool”.

This is the configuration class to store the configuration of a PatchTSMixerModel. It is used to instantiate a PatchTSMixer model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the PatchTSMixer ibm/patchtsmixer-etth1-pretrain architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import PatchTSMixerConfig, PatchTSMixerModel

    >>> # Initializing a default PatchTSMixer configuration
    >>> configuration = PatchTSMixerConfig()

    >>> # Randomly initializing a model (with random weights) from the configuration
    >>> model = PatchTSMixerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

##
PatchTSMixerModel

###
class transformers.PatchTSMixerModel

< source >

( config: PatchTSMixerConfig mask_input: bool = False )

Parameters

- config (PatchTSMixerConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- mask_input (`bool`, optional, defaults to `False`) — If True, Masking will be enabled. False otherwise.

The PatchTSMixer Model for time-series forecasting.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( past_values: Tensor observed_mask: typing.Optional[torch.Tensor] = None output_hidden_states: typing.Optional[bool] = False return_dict: typing.Optional[bool] = None ) →
`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`) — Context values of the time series. For a pretraining task, this denotes the input time series to predict the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, for classification or regression tasks, it denotes the appropriate context values of the time series.

  For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is greater than 1.

- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- observed_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`, optional) — Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected in `[0, 1]`:
  - 1 for values that are observed,
  - 0 for values that are missing (i.e. NaNs that were replaced by zeros).

Returns

`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (PatchTSMixerConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, d_model)`) — Hidden-state at the output of the last layer of the model.
- hidden_states (`tuple(torch.FloatTensor)`, optional) — Hidden-states of the model at the output of each layer.
- patch_input (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`) — Patched input data to the model.
- mask: (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches)`,optional) — Bool Tensor indicating True in masked patches and False otherwise.
- loc: (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`,optional) — Gives the mean of the context window per channel. Used for revin denorm outside the model, if revin enabled.
- scale: (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`,optional) — Gives the std dev of the context window per channel. Used for revin denorm outside the model, if revin enabled.

The PatchTSMixerModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
PatchTSMixerForPrediction

###
class transformers.PatchTSMixerForPrediction

< source >

( config: PatchTSMixerConfig )

Parameters

- config (`PatchTSMixerConfig`, required) — Configuration.

`PatchTSMixer` for forecasting application.

####
forward

< source >

( past_values: Tensor observed_mask: typing.Optional[torch.Tensor] = None future_values: typing.Optional[torch.Tensor] = None output_hidden_states: typing.Optional[bool] = False return_loss: bool = True return_dict: typing.Optional[bool] = None ) →
`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForPredictionOutput` or `tuple(torch.FloatTensor)`

Parameters

- past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`) — Context values of the time series. For a pretraining task, this denotes the input time series to predict the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, for classification or regression tasks, it denotes the appropriate context values of the time series.

  For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is greater than 1.

- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- observed_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`, optional) — Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected in `[0, 1]`:
  - 1 for values that are observed,
  - 0 for values that are missing (i.e. NaNs that were replaced by zeros).
- future_values (`torch.FloatTensor` of shape `(batch_size, target_len, num_input_channels)` for forecasting, — `(batch_size, num_targets)` for regression, or `(batch_size,)` for classification, optional): Target values of the time series, that serve as labels for the model. The `future_values` is what the Transformer needs during training to learn to output, given the `past_values`. Note that, this is NOT required for a pretraining task.

  For a forecasting task, the shape is be `(batch_size, target_len, num_input_channels)`. Even if we want to forecast only specific channels by setting the indices in `prediction_channel_indices` parameter, pass the target data with all channels, as channel Filtering for both prediction and target will be manually applied before the loss computation.

- return_loss (`bool`, optional) — Whether to return the loss in the `forward` call.

Returns

`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForPredictionOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForPredictionOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (PatchTSMixerConfig) and inputs.

- prediction_outputs (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_input_channels)`) — Prediction output from the forecast head.
- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`) — Backbone embeddings before passing through the head.
- hidden_states (`tuple(torch.FloatTensor)`, optional) — Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- loss (optional, returned when `y` is provided, `torch.FloatTensor` of shape `()`) — Total loss.
- loc (`torch.FloatTensor`, optional of shape `(batch_size, 1, num_input_channels)`) — Input mean
- scale (`torch.FloatTensor`, optional of shape `(batch_size, 1, num_input_channels)`) — Input std dev

The PatchTSMixerForPrediction forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
PatchTSMixerForTimeSeriesClassification

###
class transformers.PatchTSMixerForTimeSeriesClassification

< source >

( config: PatchTSMixerConfig )

Parameters

- config (`PatchTSMixerConfig`, required) — Configuration.

`PatchTSMixer` for classification application.

####
forward

< source >

( past_values: Tensor future_values: Tensor = None output_hidden_states: typing.Optional[bool] = False return_loss: bool = True return_dict: typing.Optional[bool] = None ) →
`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForTimeSeriesClassificationOutput` or `tuple(torch.FloatTensor)`

Parameters

- past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`) — Context values of the time series. For a pretraining task, this denotes the input time series to predict the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, for classification or regression tasks, it denotes the appropriate context values of the time series.

  For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is greater than 1.

- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- future_values (`torch.FloatTensor` of shape `(batch_size, target_len, num_input_channels)` for forecasting, — `(batch_size, num_targets)` for regression, or `(batch_size,)` for classification, optional): Target values of the time series, that serve as labels for the model. The `future_values` is what the Transformer needs during training to learn to output, given the `past_values`. Note that, this is NOT required for a pretraining task.

  For a forecasting task, the shape is be `(batch_size, target_len, num_input_channels)`. Even if we want to forecast only specific channels by setting the indices in `prediction_channel_indices` parameter, pass the target data with all channels, as channel Filtering for both prediction and target will be manually applied before the loss computation.

  For a classification task, it has a shape of `(batch_size,)`.

  For a regression task, it has a shape of `(batch_size, num_targets)`.

- return_loss (`bool`, optional) — Whether to return the loss in the `forward` call.

Returns

`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForTimeSeriesClassificationOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForTimeSeriesClassificationOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (PatchTSMixerConfig) and inputs.

- prediction_outputs (`torch.FloatTensor` of shape `(batch_size, num_labels)`) — Prediction output from the classfication head.
- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`) — Backbone embeddings before passing through the head.
- hidden_states (`tuple(torch.FloatTensor)`, optional) — Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- loss (optional, returned when `y` is provided, `torch.FloatTensor` of shape `()`) — Total loss.

The PatchTSMixerForTimeSeriesClassification forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
PatchTSMixerForPretraining

###
class transformers.PatchTSMixerForPretraining

< source >

( config: PatchTSMixerConfig )

Parameters

- config (`PatchTSMixerConfig`, required) — Configuration.

`PatchTSMixer` for mask pretraining.

####
forward

< source >

( past_values: Tensor observed_mask: typing.Optional[torch.Tensor] = None output_hidden_states: typing.Optional[bool] = False return_loss: bool = True return_dict: typing.Optional[bool] = None ) →
`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForPreTrainingOutput` or `tuple(torch.FloatTensor)`

Parameters

- past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`) — Context values of the time series. For a pretraining task, this denotes the input time series to predict the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, for classification or regression tasks, it denotes the appropriate context values of the time series.

  For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is greater than 1.

- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- observed_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`, optional) — Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected in `[0, 1]`:
  - 1 for values that are observed,
  - 0 for values that are missing (i.e. NaNs that were replaced by zeros).
- return_loss (`bool`, optional) — Whether to return the loss in the `forward` call.

Returns

`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForPreTrainingOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForPreTrainingOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (PatchTSMixerConfig) and inputs.

- prediction_outputs (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, patch_length)`) — Prediction output from the pretrain head.
- hidden_states (`tuple(torch.FloatTensor)`, optional) — Hidden-states of the model at the output of each layer.
- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`) — Backbone embeddings before passing through the head.
- loss (optional, returned when `y` is provided, `torch.FloatTensor` of shape `()`) — Total loss

The PatchTSMixerForPretraining forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
PatchTSMixerForRegression

###
class transformers.PatchTSMixerForRegression

< source >

( config: PatchTSMixerConfig )

Parameters

- config (`PatchTSMixerConfig`, required) — Configuration.

`PatchTSMixer` for regression application.

####
forward

< source >

( past_values: Tensor future_values: Tensor = None output_hidden_states: typing.Optional[bool] = False return_loss: bool = True return_dict: typing.Optional[bool] = None ) →
`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForRegressionOutput` or `tuple(torch.FloatTensor)`

Parameters

- past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`) — Context values of the time series. For a pretraining task, this denotes the input time series to predict the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, for classification or regression tasks, it denotes the appropriate context values of the time series.

  For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is greater than 1.

- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- future_values (`torch.FloatTensor` of shape `(batch_size, target_len, num_input_channels)` for forecasting, — `(batch_size, num_targets)` for regression, or `(batch_size,)` for classification, optional): Target values of the time series, that serve as labels for the model. The `future_values` is what the Transformer needs during training to learn to output, given the `past_values`. Note that, this is NOT required for a pretraining task.

  For a forecasting task, the shape is be `(batch_size, target_len, num_input_channels)`. Even if we want to forecast only specific channels by setting the indices in `prediction_channel_indices` parameter, pass the target data with all channels, as channel Filtering for both prediction and target will be manually applied before the loss computation.

  For a classification task, it has a shape of `(batch_size,)`.

  For a regression task, it has a shape of `(batch_size, num_targets)`.

- return_loss (`bool`, optional) — Whether to return the loss in the `forward` call.

Returns

`transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForRegressionOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.patchtsmixer.modeling_patchtsmixer.PatchTSMixerForRegressionOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (PatchTSMixerConfig) and inputs.

- prediction_outputs (`torch.FloatTensor` of shape `(batch_size, num_targets)`) — Prediction output from the regression head.
- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`) — Backbone embeddings before passing through the head.
- hidden_states (`tuple(torch.FloatTensor)`, optional) — Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- loss (optional, returned when `y` is provided, `torch.FloatTensor` of shape `()`) — Total loss.

The PatchTSMixerForRegression forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.