# 시계열 유틸리티 [[time-series-utilities]]

이 페이지는 시계열 기반 모델에서 사용할 수 있는 유틸리티 함수와 클래스들을 나열합니다.

이 함수들 대부분은 시계열 모델의 코드를 연구하거나 분포 출력 클래스의 컬렉션에 추가하려는 경우에만 유용합니다.

## 분포 출력 (Distributional Output) [[transformers.time_series_utils.NormalOutput]][[transformers.time_series_utils.NormalOutput]]

#### transformers.time_series_utils.NormalOutput[[transformers.time_series_utils.NormalOutput]]

```python
transformers.time_series_utils.NormalOutput(dim: int = 1)
```

[Source](https://github.com/huggingface/transformers/blob/v5.15.1/src/transformers/time_series_utils.py#L179)

Normal distribution output class.

#### Normal[[transformers.time_series_utils.NormalOutput.distribution_class]]

```python
Normal(*args, **kwargs)
```

A mock value for a dotted path (e.g. `torch.float32`): attribute access chains,
calls behave as pass-through decorators, `repr` is the dotted path, and using it
as a base class substitutes a plain-`type` base (PEP 560 `__mro_entries__`), so
real subclasses keep a normal metaclass and `inspect.signature` reads their real
`__init__` instead of a mock's.

#### transformers.time_series_utils.StudentTOutput[[transformers.time_series_utils.StudentTOutput]]

```python
transformers.time_series_utils.StudentTOutput(dim: int = 1)
```

[Source](https://github.com/huggingface/transformers/blob/v5.15.1/src/transformers/time_series_utils.py#L164)

Student-T distribution output class.

#### StudentT[[transformers.time_series_utils.StudentTOutput.distribution_class]]

```python
StudentT(*args, **kwargs)
```

A mock value for a dotted path (e.g. `torch.float32`): attribute access chains,
calls behave as pass-through decorators, `repr` is the dotted path, and using it
as a base class substitutes a plain-`type` base (PEP 560 `__mro_entries__`), so
real subclasses keep a normal metaclass and `inspect.signature` reads their real
`__init__` instead of a mock's.

#### transformers.time_series_utils.NegativeBinomialOutput[[transformers.time_series_utils.NegativeBinomialOutput]]

```python
transformers.time_series_utils.NegativeBinomialOutput(dim: int = 1)
```

[Source](https://github.com/huggingface/transformers/blob/v5.15.1/src/transformers/time_series_utils.py#L193)

Negative Binomial distribution output class.

#### NegativeBinomial[[transformers.time_series_utils.NegativeBinomialOutput.distribution_class]]

```python
NegativeBinomial(*args, **kwargs)
```

A mock value for a dotted path (e.g. `torch.float32`): attribute access chains,
calls behave as pass-through decorators, `repr` is the dotted path, and using it
as a base class substitutes a plain-`type` base (PEP 560 `__mro_entries__`), so
real subclasses keep a normal metaclass and `inspect.signature` reads their real
`__init__` instead of a mock's.