#
시계열 트랜스포머

##
개요

이 시계열 트랜스포머 모델은 시계열 예측을 위한 기본적인 인코더-디코더 구조의 트랜스포머 입니다. 이 모델은 kashif에 의해 기여되었습니다.

##
사용 팁

- 다른 라이브러리의 모델들과 마찬가지로, TimeSeriesTransformerModel은 상단에 헤드가 없는 기본적인 트랜스포머 입니다. TimeSeriesTransformerForPrediction은 상단에 분포 헤드를 추가하여 시계열 예측에 사용할 수 있습니다. 이 모델은 이른바 확률적 예측 모델이며, 포인트 예측 모델이 아닙니다. 즉 샘플링할 수 있는 분포를 학습하며, 값을 직접 출력 하지는 않습니다.
- TimeSeriesTransformerForPrediction은 두개의 블록으로 구성되어 있습니다. 인코더는 `context_length`의 시계열 값을 입력(`past_values`라고 부름)으로 받아들이며, 디코더는 미래의 `prediction_length`만큼 시계열 값을 예측합니다(`future_values`라고 부름). 학습중에는 모델에 `past_values` 와 `future_values`쌍을 모델에 제공해야 합니다.
- 가공하지 않은 `past_values` 와 `future_values` 쌍 외에도, 일반적으로 모델에 추가적인 특징을 제공합니다. 다음은 그 특징들에 대해 소개합니다:
  - `past_time_features`: 모델이 `past_values`에 추가할 시간적 특성. 이는 트랜스포머 인코더의 “위치 인코딩” 역할을 합니다. 예를 들어 “월의 일”, “연도의 월” 등을 스칼라 값으로 (그리고 벡터로 쌓아서) 나타냅니다. 예시: 특정 시계열 값이 8월 11일에 기록되었다면, [11, 8]을 시간 특성 벡터로 사용할 수 있습니다 (11은 “월의 일”, 8은 “연도의 월”).
  - `future_time_features`: 모델이 `future_values`에 추가할 시간적 특성. 이는 트랜스포머 디코더의 “위치 인코딩” 역할을 합니다. 예를 들어 “월의 일”, “연도의 월” 등을 스칼라 값으로 (그리고 벡터로 쌓아서) 나타냅니다. 예: 특정 시계열 값이 8월 11일에 얻어졌다면, [11, 8]을 시간 특성 벡터로 사용할 수 있습니다 (11은 “월의 일”, 8은 “연도의 월”).
  - `static_categorical_features`: 시간에 따라 변하지 않는 범주형 특성 (즉, 모든 `past_values`와 `future_values`에 대해 동일한 값을 가짐). 예를 들어 특정 시계열을 식별하는 매장 ID나 지역 ID가 있습니다. 이러한 특성은 모든 데이터 포인트(미래의 데이터 포인트 포함)에 대해 알려져 있어야 합니다.
  - `static_real_features`: 시간에 따라 변하지 않는 실수값 특성 (즉, 모든 `past_values`와 `future_values`에 대해 동일한 값을 가짐). 예를 들어 시계열 값을 가진 제품의 이미지 표현 (시계열이 신발 판매에 관한 것이라면 “신발” 사진의 ResNet 임베딩 처럼)이 있습니다. 이러한 특성은 모든 데이터 포인트(미래의 데이터 포인트 포함)에 대해 알려져 있어야 합니다.
- 이 모델은 기계 번역을 위한 트랜스포머 훈련과 유사하게 “교사 강제(teacher-forcing)” 방식으로 훈련됩니다. 즉, 훈련 중에 `future_values`를 디코더의 입력으로 오른쪽으로 한 위치 이동시키고, `past_values`의 마지막 값을 앞에 붙입니다. 각 시간 단계에서 모델은 다음 타겟을 예측해야 합니다. 따라서 훈련 설정은 언어를 위한 GPT 모델과 유사하지만, `decoder_start_token_id` 개념이 없습니다 (우리는 단순히 컨텍스트의 마지막 값을 디코더의 초기 입력으로 사용합니다).
- 추론 시에는 `past_values`의 최종 값을 디코더의 입력으로 제공합니다. 그 다음, 모델에서 샘플링하여 다음 시간 단계에서의 예측을 만들고, 이를 디코더에 공급하여 다음 예측을 만듭니다 (자기회귀 생성이라고도 함).

##
자료

시작하는 데 도움이 되는 Hugging Face와 community 자료 목록(🌎로 표시됨) 입니다. 여기에 포함될 자료를 제출하고 싶으시다면 PR(Pull Request)를 열어주세요. 리뷰 해드리겠습니다! 자료는 기존 자료를 복제하는 대신 새로운 내용을 담고 있어야 합니다.

- HuggingFace 블로그에서 시계열 트랜스포머 포스트를 확인하세요: 🤗 트랜스포머와 확률적 시계열 예측

##
TimeSeriesTransformerConfig

###
class transformers.TimeSeriesTransformerConfig

< source >

( prediction_length: typing.Optional[int] = None context_length: typing.Optional[int] = None distribution_output: str = 'student_t' loss: str = 'nll' input_size: int = 1 lags_sequence: typing.List[int] = [1, 2, 3, 4, 5, 6, 7] scaling: typing.Union[str, bool, NoneType] = 'mean' num_dynamic_real_features: int = 0 num_static_categorical_features: int = 0 num_static_real_features: int = 0 num_time_features: int = 0 cardinality: typing.Optional[typing.List[int]] = None embedding_dimension: typing.Optional[typing.List[int]] = None encoder_ffn_dim: int = 32 decoder_ffn_dim: int = 32 encoder_attention_heads: int = 2 decoder_attention_heads: int = 2 encoder_layers: int = 2 decoder_layers: int = 2 is_encoder_decoder: bool = True activation_function: str = 'gelu' d_model: int = 64 dropout: float = 0.1 encoder_layerdrop: float = 0.1 decoder_layerdrop: float = 0.1 attention_dropout: float = 0.1 activation_dropout: float = 0.1 num_parallel_samples: int = 100 init_std: float = 0.02 use_cache = True **kwargs )

Parameters

- prediction_length (`int`) — The prediction length for the decoder. In other words, the prediction horizon of the model. This value is typically dictated by the dataset and we recommend to set it appropriately.
- context_length (`int`, optional, defaults to `prediction_length`) — The context length for the encoder. If `None`, the context length will be the same as the `prediction_length`.
- distribution_output (`string`, optional, defaults to `"student_t"`) — The distribution emission head for the model. Could be either “student_t”, “normal” or “negative_binomial”.
- loss (`string`, optional, defaults to `"nll"`) — The loss function for the model corresponding to the `distribution_output` head. For parametric distributions it is the negative log likelihood (nll) - which currently is the only supported one.
- input_size (`int`, optional, defaults to 1) — The size of the target variable which by default is 1 for univariate targets. Would be > 1 in case of multivariate targets.
- scaling (`string` or `bool`, optional defaults to `"mean"`) — Whether to scale the input targets via “mean” scaler, “std” scaler or no scaler if `None`. If `True`, the scaler is set to “mean”.
- lags_sequence (`list[int]`, optional, defaults to `[1, 2, 3, 4, 5, 6, 7]`) — The lags of the input time series as covariates often dictated by the frequency of the data. Default is `[1, 2, 3, 4, 5, 6, 7]` but we recommend to change it based on the dataset appropriately.
- num_time_features (`int`, optional, defaults to 0) — The number of time features in the input time series.
- num_dynamic_real_features (`int`, optional, defaults to 0) — The number of dynamic real valued features.
- num_static_categorical_features (`int`, optional, defaults to 0) — The number of static categorical features.
- num_static_real_features (`int`, optional, defaults to 0) — The number of static real valued features.
- cardinality (`list[int]`, optional) — The cardinality (number of different values) for each of the static categorical features. Should be a list of integers, having the same length as `num_static_categorical_features`. Cannot be `None` if `num_static_categorical_features` is > 0.
- embedding_dimension (`list[int]`, optional) — The dimension of the embedding for each of the static categorical features. Should be a list of integers, having the same length as `num_static_categorical_features`. Cannot be `None` if `num_static_categorical_features` is > 0.
- d_model (`int`, optional, defaults to 64) — Dimensionality of the transformer layers.
- encoder_layers (`int`, optional, defaults to 2) — Number of encoder layers.
- decoder_layers (`int`, optional, defaults to 2) — Number of decoder layers.
- encoder_attention_heads (`int`, optional, defaults to 2) — Number of attention heads for each attention layer in the Transformer encoder.
- decoder_attention_heads (`int`, optional, defaults to 2) — Number of attention heads for each attention layer in the Transformer decoder.
- encoder_ffn_dim (`int`, optional, defaults to 32) — Dimension of the “intermediate” (often named feed-forward) layer in encoder.
- decoder_ffn_dim (`int`, optional, defaults to 32) — Dimension of the “intermediate” (often named feed-forward) layer in decoder.
- activation_function (`str` or `function`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and decoder. If string, `"gelu"` and `"relu"` are supported.
- dropout (`float`, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the encoder, and decoder.
- encoder_layerdrop (`float`, optional, defaults to 0.1) — The dropout probability for the attention and fully connected layers for each encoder layer.
- decoder_layerdrop (`float`, optional, defaults to 0.1) — The dropout probability for the attention and fully connected layers for each decoder layer.
- attention_dropout (`float`, optional, defaults to 0.1) — The dropout probability for the attention probabilities.
- activation_dropout (`float`, optional, defaults to 0.1) — The dropout probability used between the two layers of the feed-forward networks.
- num_parallel_samples (`int`, optional, defaults to 100) — The number of samples to generate in parallel for each time step of inference.
- init_std (`float`, optional, defaults to 0.02) — The standard deviation of the truncated normal weight initialization distribution.
- use_cache (`bool`, optional, defaults to `True`) — Whether to use the past key/values attentions (if applicable to the model) to speed up decoding.
- Example —

This is the configuration class to store the configuration of a TimeSeriesTransformerModel. It is used to instantiate a Time Series Transformer 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 Time Series Transformer huggingface/time-series-transformer-tourism-monthly architecture.

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

    >>> from transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerModel

    >>> # Initializing a Time Series Transformer configuration with 12 time steps for prediction
    >>> configuration = TimeSeriesTransformerConfig(prediction_length=12)

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

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

##
TimeSeriesTransformerModel

###
class transformers.TimeSeriesTransformerModel

< source >

( config: TimeSeriesTransformerConfig )

Parameters

- config (TimeSeriesTransformerConfig) — 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.

The bare Time Series Transformer Model outputting raw hidden-states without any specific head on top.

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 past_time_features: Tensor past_observed_mask: Tensor static_categorical_features: typing.Optional[torch.Tensor] = None static_real_features: typing.Optional[torch.Tensor] = None future_values: typing.Optional[torch.Tensor] = None future_time_features: typing.Optional[torch.Tensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None output_hidden_states: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None use_cache: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None ) →
transformers.modeling_outputs.Seq2SeqTSModelOutput or `tuple(torch.FloatTensor)`

Parameters

- past_values (`torch.Tensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`) — Past values of the time series, that serve as context in order to predict the future. The sequence size of this tensor must be larger than the `context_length` of the model, since the model will use the larger size to construct lag features, i.e. additional values from the past which are added in order to serve as “extra context”.

  The `sequence_length` here is equal to `config.context_length` + `max(config.lags_sequence)`, which if no `lags_sequence` is configured, is equal to `config.context_length` + 7 (as by default, the largest look-back index in `config.lags_sequence` is 7). The property `_past_length` returns the actual length of the past.

  The `past_values` is what the Transformer encoder gets as input (with optional additional features, such as `static_categorical_features`, `static_real_features`, `past_time_features` and lags).

  Optionally, missing values need to be replaced with zeros and indicated via the `past_observed_mask`.

  For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number of variates in the time series per time step.

- past_time_features (`torch.Tensor` of shape `(batch_size, sequence_length, num_features)`) — Required time features, which the model internally will add to `past_values`. These could be things like “month of year”, “day of the month”, etc. encoded as vectors (for instance as Fourier features). These could also be so-called “age” features, which basically help the model know “at which point in life” a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. Holiday features are also a good example of time features.

  These features serve as the “positional encodings” of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional time features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`.

  Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these features must but known at prediction time.

  The `num_features` here is equal to `config.`num_time_features`+`config.num_dynamic_real_features`.

- past_observed_mask (`torch.Tensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`, 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).
- static_categorical_features (`torch.Tensor` of shape `(batch_size, number of static categorical features)`, optional) — Optional static categorical features for which the model will learn an embedding, which it will add to the values of the time series.

  Static categorical features are features which have the same value for all time steps (static over time).

  A typical example of a static categorical feature is a time series ID.

- static_real_features (`torch.Tensor` of shape `(batch_size, number of static real features)`, optional) — Optional static real features which the model will add to the values of the time series.

  Static real features are features which have the same value for all time steps (static over time).

  A typical example of a static real feature is promotion information.

- future_values (`torch.Tensor` of shape `(batch_size, prediction_length)` or `(batch_size, prediction_length, input_size)`, optional) — Future 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`.

  The sequence length here is equal to `prediction_length`.

  See the demo notebook and code snippets for details.

  Optionally, during training any missing values need to be replaced with zeros and indicated via the `future_observed_mask`.

  For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number of variates in the time series per time step.

- future_time_features (`torch.Tensor` of shape `(batch_size, prediction_length, num_features)`) — Required time features for the prediction window, which the model internally will add to `future_values`. These could be things like “month of year”, “day of the month”, etc. encoded as vectors (for instance as Fourier features). These could also be so-called “age” features, which basically help the model know “at which point in life” a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. Holiday features are also a good example of time features.

  These features serve as the “positional encodings” of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional time features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`.

  Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these features must but known at prediction time.

  The `num_features` here is equal to `config.`num_time_features`+`config.num_dynamic_real_features`.

- decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to make sure the model can only look at previous inputs in order to predict the future.
- head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- encoder_outputs (`List[torch.FloatTensor]`)`, *optional*) -- Tuple consists of` last_hidden_state`,` hidden_states`(*optional*) and`attentions`(*optional*)`last_hidden_state`of shape`(batch_size, sequence_length, hidden_size)` (optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- past_key_values (`List[torch.FloatTensor]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

transformers.modeling_outputs.Seq2SeqTSModelOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.Seq2SeqTSModelOutput 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 (TimeSeriesTransformerConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output.

- past_key_values (`EncoderDecoderCache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — It is a EncoderDecoderCache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

- decoder_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.

- decoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

- encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.

- encoder_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.

- encoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, optional) — Shift values of each time series’ context window which is used to give the model inputs of the same magnitude and then used to shift back to the original magnitude.

- scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, optional) — Scaling values of each time series’ context window which is used to give the model inputs of the same magnitude and then used to rescale back to the original magnitude.

- static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, optional) — Static features of each time series’ in a batch which are copied to the covariates at inference time.

The TimeSeriesTransformerModel 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.

Examples:

    >>> from huggingface_hub import hf_hub_download
    >>> import torch
    >>> from transformers import TimeSeriesTransformerModel

    >>> file = hf_hub_download(
    ...     repo_id="hf-internal-testing/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset"
    ... )
    >>> batch = torch.load(file)

    >>> model = TimeSeriesTransformerModel.from_pretrained("huggingface/time-series-transformer-tourism-monthly")

    >>> # during training, one provides both past and future values
    >>> # as well as possible additional features
    >>> outputs = model(
    ...     past_values=batch["past_values"],
    ...     past_time_features=batch["past_time_features"],
    ...     past_observed_mask=batch["past_observed_mask"],
    ...     static_categorical_features=batch["static_categorical_features"],
    ...     static_real_features=batch["static_real_features"],
    ...     future_values=batch["future_values"],
    ...     future_time_features=batch["future_time_features"],
    ... )

    >>> last_hidden_state = outputs.last_hidden_state

##
TimeSeriesTransformerForPrediction

###
class transformers.TimeSeriesTransformerForPrediction

< source >

( config: TimeSeriesTransformerConfig )

Parameters

- config (TimeSeriesTransformerConfig) — 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.

The Time Series Transformer Model with a distribution head on top 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 past_time_features: Tensor past_observed_mask: Tensor static_categorical_features: typing.Optional[torch.Tensor] = None static_real_features: typing.Optional[torch.Tensor] = None future_values: typing.Optional[torch.Tensor] = None future_time_features: typing.Optional[torch.Tensor] = None future_observed_mask: typing.Optional[torch.Tensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None output_hidden_states: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None use_cache: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None ) →
transformers.modeling_outputs.Seq2SeqTSModelOutput or `tuple(torch.FloatTensor)`

Parameters

- past_values (`torch.Tensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`) — Past values of the time series, that serve as context in order to predict the future. The sequence size of this tensor must be larger than the `context_length` of the model, since the model will use the larger size to construct lag features, i.e. additional values from the past which are added in order to serve as “extra context”.

  The `sequence_length` here is equal to `config.context_length` + `max(config.lags_sequence)`, which if no `lags_sequence` is configured, is equal to `config.context_length` + 7 (as by default, the largest look-back index in `config.lags_sequence` is 7). The property `_past_length` returns the actual length of the past.

  The `past_values` is what the Transformer encoder gets as input (with optional additional features, such as `static_categorical_features`, `static_real_features`, `past_time_features` and lags).

  Optionally, missing values need to be replaced with zeros and indicated via the `past_observed_mask`.

  For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number of variates in the time series per time step.

- past_time_features (`torch.Tensor` of shape `(batch_size, sequence_length, num_features)`) — Required time features, which the model internally will add to `past_values`. These could be things like “month of year”, “day of the month”, etc. encoded as vectors (for instance as Fourier features). These could also be so-called “age” features, which basically help the model know “at which point in life” a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. Holiday features are also a good example of time features.

  These features serve as the “positional encodings” of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional time features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`.

  Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these features must but known at prediction time.

  The `num_features` here is equal to `config.`num_time_features`+`config.num_dynamic_real_features`.

- past_observed_mask (`torch.Tensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`, 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).
- static_categorical_features (`torch.Tensor` of shape `(batch_size, number of static categorical features)`, optional) — Optional static categorical features for which the model will learn an embedding, which it will add to the values of the time series.

  Static categorical features are features which have the same value for all time steps (static over time).

  A typical example of a static categorical feature is a time series ID.

- static_real_features (`torch.Tensor` of shape `(batch_size, number of static real features)`, optional) — Optional static real features which the model will add to the values of the time series.

  Static real features are features which have the same value for all time steps (static over time).

  A typical example of a static real feature is promotion information.

- future_values (`torch.Tensor` of shape `(batch_size, prediction_length)` or `(batch_size, prediction_length, input_size)`, optional) — Future 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`.

  The sequence length here is equal to `prediction_length`.

  See the demo notebook and code snippets for details.

  Optionally, during training any missing values need to be replaced with zeros and indicated via the `future_observed_mask`.

  For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number of variates in the time series per time step.

- future_time_features (`torch.Tensor` of shape `(batch_size, prediction_length, num_features)`) — Required time features for the prediction window, which the model internally will add to `future_values`. These could be things like “month of year”, “day of the month”, etc. encoded as vectors (for instance as Fourier features). These could also be so-called “age” features, which basically help the model know “at which point in life” a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. Holiday features are also a good example of time features.

  These features serve as the “positional encodings” of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional time features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`.

  Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these features must but known at prediction time.

  The `num_features` here is equal to `config.`num_time_features`+`config.num_dynamic_real_features`.

- future_observed_mask (`torch.Tensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`, optional) — Boolean mask to indicate which `future_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).

  This mask is used to filter out missing values for the final loss calculation.

- decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to make sure the model can only look at previous inputs in order to predict the future.
- head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- encoder_outputs (`List[torch.FloatTensor]`)`, *optional*) -- Tuple consists of` last_hidden_state`,` hidden_states`(*optional*) and`attentions`(*optional*)`last_hidden_state`of shape`(batch_size, sequence_length, hidden_size)` (optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- past_key_values (`List[torch.FloatTensor]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

transformers.modeling_outputs.Seq2SeqTSModelOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.Seq2SeqTSModelOutput 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 (TimeSeriesTransformerConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output.

- past_key_values (`EncoderDecoderCache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — It is a EncoderDecoderCache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

- decoder_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.

- decoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

- encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.

- encoder_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.

- encoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, optional) — Shift values of each time series’ context window which is used to give the model inputs of the same magnitude and then used to shift back to the original magnitude.

- scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, optional) — Scaling values of each time series’ context window which is used to give the model inputs of the same magnitude and then used to rescale back to the original magnitude.

- static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, optional) — Static features of each time series’ in a batch which are copied to the covariates at inference time.

The TimeSeriesTransformerForPrediction 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.

Examples:

    >>> from huggingface_hub import hf_hub_download
    >>> import torch
    >>> from transformers import TimeSeriesTransformerForPrediction

    >>> file = hf_hub_download(
    ...     repo_id="hf-internal-testing/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset"
    ... )
    >>> batch = torch.load(file)

    >>> model = TimeSeriesTransformerForPrediction.from_pretrained(
    ...     "huggingface/time-series-transformer-tourism-monthly"
    ... )

    >>> # during training, one provides both past and future values
    >>> # as well as possible additional features
    >>> outputs = model(
    ...     past_values=batch["past_values"],
    ...     past_time_features=batch["past_time_features"],
    ...     past_observed_mask=batch["past_observed_mask"],
    ...     static_categorical_features=batch["static_categorical_features"],
    ...     static_real_features=batch["static_real_features"],
    ...     future_values=batch["future_values"],
    ...     future_time_features=batch["future_time_features"],
    ... )

    >>> loss = outputs.loss
    >>> loss.backward()

    >>> # during inference, one only provides past values
    >>> # as well as possible additional features
    >>> # the model autoregressively generates future values
    >>> outputs = model.generate(
    ...     past_values=batch["past_values"],
    ...     past_time_features=batch["past_time_features"],
    ...     past_observed_mask=batch["past_observed_mask"],
    ...     static_categorical_features=batch["static_categorical_features"],
    ...     static_real_features=batch["static_real_features"],
    ...     future_time_features=batch["future_time_features"],
    ... )

    >>> mean_prediction = outputs.sequences.mean(dim=1)

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