> ## 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.

# Data

> Utilies for generating time series datasets

### `generate_series`

```python theme={null}
generate_series(n_series, freq='D', min_length=50, max_length=500, n_static_features=0, equal_ends=False, with_trend=False, static_as_categorical=True, n_models=0, level=None, engine='pandas', seed=0)
```

Generate Synthetic Panel Series.

**Parameters:**

| Name                    | Type                       | Description                                                                                                         | Default               |
| ----------------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------- | --------------------- |
| `n_series`              | <code>[int](#int)</code>   | Number of series for synthetic panel.                                                                               | *required*            |
| `freq`                  | <code>[str](#str)</code>   | Frequency of the data (pandas alias). Seasonalities are implemented for hourly, daily and monthly. Defaults to 'D'. | <code>'D'</code>      |
| `min_length`            | <code>[int](#int)</code>   | Minimum length of synthetic panel's series. Defaults to 50.                                                         | <code>50</code>       |
| `max_length`            | <code>[int](#int)</code>   | Maximum length of synthetic panel's series. Defaults to 500.                                                        | <code>500</code>      |
| `n_static_features`     | <code>[int](#int)</code>   | Number of static exogenous variables for synthetic panel's series. Defaults to 0.                                   | <code>0</code>        |
| `equal_ends`            | <code>[bool](#bool)</code> | Series should end in the same timestamp. Defaults to False.                                                         | <code>False</code>    |
| `with_trend`            | <code>[bool](#bool)</code> | Series should have a (positive) trend. Defaults to False.                                                           | <code>False</code>    |
| `static_as_categorical` | <code>[bool](#bool)</code> | Static features should have a categorical data type. Defaults to True.                                              | <code>True</code>     |
| `n_models`              | <code>[int](#int)</code>   | Number of models predictions to simulate. Defaults to 0.                                                            | <code>0</code>        |
| `level`                 | <code>list of float</code> | Confidence level for intervals to simulate for each model. Defaults to None.                                        | <code>None</code>     |
| `engine`                | <code>[str](#str)</code>   | Output Dataframe type. Defaults to 'pandas'.                                                                        | <code>'pandas'</code> |
| `seed`                  | <code>[int](#int)</code>   | Random seed used for generating the data. Defaults to 0.                                                            | <code>0</code>        |

**Returns:**

| Type                                                      | Description                                                                                                |
| --------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| <code>[DataFrame](#utilsforecast.compat.DataFrame)</code> | pandas or polars DataFrame: Synthetic panel with columns \[`unique_id`, `ds`, `y`] and exogenous features. |