> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Feature Engineering | UtilsForecast

> Create exogenous regressors for your models

### `fourier`

```python theme={null}
fourier(df, freq, season_length, k, h=0, id_col='unique_id', time_col='ds')
```

Compute fourier seasonal terms for training and forecasting

**Parameters:**

| Name            | Type                                    | Description                                                                                       | Default                   |
| --------------- | --------------------------------------- | ------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`            | <code>pandas or polars DataFrame</code> | Dataframe with ids, times and values for the exogenous regressors.                                | *required*                |
| `freq`          | <code>[str](#str) or [int](#int)</code> | Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.              | *required*                |
| `season_length` | <code>[int](#int)</code>                | Number of observations per unit of time. Ex: 24 Hourly data.                                      | *required*                |
| `k`             | <code>[int](#int)</code>                | Maximum order of the fourier terms                                                                | *required*                |
| `h`             | <code>[int](#int)</code>                | Forecast horizon. Defaults to 0.                                                                  | <code>0</code>            |
| `id_col`        | <code>[str](#str)</code>                | Column that identifies each serie. Defaults to 'unique\_id'.                                      | <code>'unique\_id'</code> |
| `time_col`      | <code>[str](#str)</code>                | Column that identifies each timestep, its values can be timestamps or integers. Defaults to 'ds'. | <code>'ds'</code>         |

**Returns:**

| Type                                                                                                                 | Description                                                                                                                                                            |
| -------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <code>[Tuple](#typing.Tuple)\[[DFType](#utilsforecast.compat.DFType), [DFType](#utilsforecast.compat.DFType)]</code> | tuple\[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values. |

### `trend`

```python theme={null}
trend(df, freq, h=0, id_col='unique_id', time_col='ds')
```

Add a trend column with consecutive integers for training and forecasting

**Parameters:**

| Name       | Type                                    | Description                                                                                       | Default                   |
| ---------- | --------------------------------------- | ------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`       | <code>pandas or polars DataFrame</code> | Dataframe with ids, times and values for the exogenous regressors.                                | *required*                |
| `freq`     | <code>[str](#str) or [int](#int)</code> | Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.              | *required*                |
| `h`        | <code>[int](#int)</code>                | Forecast horizon. Defaults to 0.                                                                  | <code>0</code>            |
| `id_col`   | <code>[str](#str)</code>                | Column that identifies each serie. Defaults to 'unique\_id'.                                      | <code>'unique\_id'</code> |
| `time_col` | <code>[str](#str)</code>                | Column that identifies each timestep, its values can be timestamps or integers. Defaults to 'ds'. | <code>'ds'</code>         |

**Returns:**

| Type                                                                                                                 | Description                                                                                                                                                            |
| -------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <code>[Tuple](#typing.Tuple)\[[DFType](#utilsforecast.compat.DFType), [DFType](#utilsforecast.compat.DFType)]</code> | tuple\[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values. |

### `time_features`

```python theme={null}
time_features(df, freq, features, h=0, id_col='unique_id', time_col='ds')
```

Compute timestamp-based features for training and forecasting

**Parameters:**

| Name       | Type                                    | Description                                                                                            | Default                   |
| ---------- | --------------------------------------- | ------------------------------------------------------------------------------------------------------ | ------------------------- |
| `df`       | <code>pandas or polars DataFrame</code> | Dataframe with ids, times and values for the exogenous regressors.                                     | *required*                |
| `freq`     | <code>[str](#str) or [int](#int)</code> | Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.                   | *required*                |
| `features` | <code>list of str or callable</code>    | Features to compute. Can be string aliases of timestamp attributes or functions to apply to the times. | *required*                |
| `h`        | <code>[int](#int)</code>                | Forecast horizon. Defaults to 0.                                                                       | <code>0</code>            |
| `id_col`   | <code>[str](#str)</code>                | Column that identifies each serie. Defaults to 'unique\_id'.                                           | <code>'unique\_id'</code> |
| `time_col` | <code>[str](#str)</code>                | Column that identifies each timestep, its values can be timestamps or integers. Defaults to 'ds'.      | <code>'ds'</code>         |

**Returns:**

| Type                                                                                                                 | Description                                                                                                                                                            |
| -------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <code>[Tuple](#typing.Tuple)\[[DFType](#utilsforecast.compat.DFType), [DFType](#utilsforecast.compat.DFType)]</code> | tuple\[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values. |

### `future_exog_to_historic`

```python theme={null}
future_exog_to_historic(df, freq, features, h=0, id_col='unique_id', time_col='ds')
```

Turn future exogenous features into historic by shifting them `h` steps.

**Parameters:**

| Name       | Type                                    | Description                                                                                       | Default                   |
| ---------- | --------------------------------------- | ------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`       | <code>pandas or polars DataFrame</code> | Dataframe with ids, times and values for the exogenous regressors.                                | *required*                |
| `freq`     | <code>[str](#str) or [int](#int)</code> | Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.              | *required*                |
| `features` | <code>list of str</code>                | Features to be converted into historic.                                                           | *required*                |
| `h`        | <code>[int](#int)</code>                | Forecast horizon. Defaults to 0.                                                                  | <code>0</code>            |
| `id_col`   | <code>[str](#str)</code>                | Column that identifies each serie. Defaults to 'unique\_id'.                                      | <code>'unique\_id'</code> |
| `time_col` | <code>[str](#str)</code>                | Column that identifies each timestep, its values can be timestamps or integers. Defaults to 'ds'. | <code>'ds'</code>         |

**Returns:**

| Type                                                                                                                 | Description                                                                                                                                                            |
| -------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <code>[Tuple](#typing.Tuple)\[[DFType](#utilsforecast.compat.DFType), [DFType](#utilsforecast.compat.DFType)]</code> | tuple\[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values. |

### `pipeline`

```python theme={null}
pipeline(df, features, freq, h=0, id_col='unique_id', time_col='ds')
```

Compute several features for training and forecasting

**Parameters:**

| Name       | Type                                    | Description                                                                                                     | Default                   |
| ---------- | --------------------------------------- | --------------------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`       | <code>pandas or polars DataFrame</code> | Dataframe with ids, times and values for the exogenous regressors.                                              | *required*                |
| `features` | <code>list of callable</code>           | List of features to compute. Must take only df, freq, h, id\_col and time\_col (other arguments must be fixed). | *required*                |
| `freq`     | <code>[str](#str) or [int](#int)</code> | Frequency of the data. Must be a valid pandas or polars offset alias, or an integer.                            | *required*                |
| `h`        | <code>[int](#int)</code>                | Forecast horizon. Defaults to 0.                                                                                | <code>0</code>            |
| `id_col`   | <code>[str](#str)</code>                | Column that identifies each serie. Defaults to 'unique\_id'.                                                    | <code>'unique\_id'</code> |
| `time_col` | <code>[str](#str)</code>                | Column that identifies each timestep, its values can be timestamps or integers. Defaults to 'ds'.               | <code>'ds'</code>         |

**Returns:**

| Type                                                                                                                 | Description                                                                                                                                                            |
| -------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <code>[Tuple](#typing.Tuple)\[[DFType](#utilsforecast.compat.DFType), [DFType](#utilsforecast.compat.DFType)]</code> | tuple\[pandas or polars DataFrame, pandas or polars DataFrame]: A tuple containing the original DataFrame with the computed features and DataFrame with future values. |