> ## 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.

# Utils | MLForecast

```python theme={null}
from fastcore.test import test_eq, test_fail
from nbdev import show_doc
```

### `generate_daily_series`

```python theme={null}
generate_daily_series(
    n_series,
    min_length=50,
    max_length=500,
    n_static_features=0,
    equal_ends=False,
    static_as_categorical=True,
    with_trend=False,
    seed=0,
    engine="pandas",
)
```

Generate Synthetic Panel Series.

**Parameters:**

| Name                    | Type                               | Description                                                        | Default               |
| ----------------------- | ---------------------------------- | ------------------------------------------------------------------ | --------------------- |
| `n_series`              | <code>[int](#int)</code>           | Number of series for synthetic panel.                              | *required*            |
| `min_length`            | <code>int, default=50</code>       | Minimum length of synthetic panel's series.                        | <code>50</code>       |
| `max_length`            | <code>int, default=500</code>      | Maximum length of synthetic panel's series.                        | <code>500</code>      |
| `n_static_features`     | <code>int, default=0</code>        | Number of static exogenous variables for synthetic panel's series. | <code>0</code>        |
| `equal_ends`            | <code>bool, default=False</code>   | Series should end in the same date stamp `ds`.                     | <code>False</code>    |
| `static_as_categorical` | <code>bool, default=True</code>    | Static features should have a categorical data type.               | <code>True</code>     |
| `with_trend`            | <code>bool, default=False</code>   | Series should have a (positive) trend.                             | <code>False</code>    |
| `seed`                  | <code>int, default=0</code>        | Random seed used for generating the data.                          | <code>0</code>        |
| `engine`                | <code>str, default='pandas'</code> | Output Dataframe type.                                             | <code>'pandas'</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. |

Generate 20 series with lengths between 100 and 1,000.

```python theme={null}
n_series = 20
min_length = 100
max_length = 1000

series = generate_daily_series(n_series, min_length, max_length)
series
```

|       | unique\_id | ds         | y        |
| ----- | ---------- | ---------- | -------- |
| 0     | id\_00     | 2000-01-01 | 0.395863 |
| 1     | id\_00     | 2000-01-02 | 1.264447 |
| 2     | id\_00     | 2000-01-03 | 2.284022 |
| 3     | id\_00     | 2000-01-04 | 3.462798 |
| 4     | id\_00     | 2000-01-05 | 4.035518 |
| ...   | ...        | ...        | ...      |
| 12446 | id\_19     | 2002-03-11 | 0.309275 |
| 12447 | id\_19     | 2002-03-12 | 1.189464 |
| 12448 | id\_19     | 2002-03-13 | 2.325032 |
| 12449 | id\_19     | 2002-03-14 | 3.333198 |
| 12450 | id\_19     | 2002-03-15 | 4.306117 |

We can also add static features to each serie (these can be things like
product\_id or store\_id). Only the first static feature (`static_0`) is
relevant to the target.

```python theme={null}
n_static_features = 2

series_with_statics = generate_daily_series(n_series, min_length, max_length, n_static_features)
series_with_statics
```

|       | unique\_id | ds         | y          | static\_0 | static\_1 |
| ----- | ---------- | ---------- | ---------- | --------- | --------- |
| 0     | id\_00     | 2000-01-01 | 7.521388   | 18        | 10        |
| 1     | id\_00     | 2000-01-02 | 24.024502  | 18        | 10        |
| 2     | id\_00     | 2000-01-03 | 43.396423  | 18        | 10        |
| 3     | id\_00     | 2000-01-04 | 65.793168  | 18        | 10        |
| 4     | id\_00     | 2000-01-05 | 76.674843  | 18        | 10        |
| ...   | ...        | ...        | ...        | ...       | ...       |
| 12446 | id\_19     | 2002-03-11 | 27.834771  | 89        | 42        |
| 12447 | id\_19     | 2002-03-12 | 107.051746 | 89        | 42        |
| 12448 | id\_19     | 2002-03-13 | 209.252845 | 89        | 42        |
| 12449 | id\_19     | 2002-03-14 | 299.987801 | 89        | 42        |
| 12450 | id\_19     | 2002-03-15 | 387.550536 | 89        | 42        |

```python theme={null}
for i in range(n_static_features):
    assert all(series_with_statics.groupby('unique_id')[f'static_{i}'].nunique() == 1)
```

If `equal_ends=False` (the default) then every serie has a different end
date.

```python theme={null}
assert series_with_statics.groupby('unique_id')['ds'].max().nunique() > 1
```

We can have all of them end at the same date by specifying
`equal_ends=True`.

```python theme={null}
series_equal_ends = generate_daily_series(n_series, min_length, max_length, equal_ends=True)

assert series_equal_ends.groupby('unique_id')['ds'].max().nunique() == 1
```

***

### `generate_prices_for_series`

```python theme={null}
generate_prices_for_series(series, horizon=7, seed=0)
```

```python theme={null}
series_for_prices = generate_daily_series(20, n_static_features=2, equal_ends=True)
series_for_prices.rename(columns={'static_1': 'product_id'}, inplace=True)
prices_catalog = generate_prices_for_series(series_for_prices, horizon=7)
prices_catalog
```

|      | ds         | unique\_id | price    |
| ---- | ---------- | ---------- | -------- |
| 0    | 2000-10-05 | id\_00     | 0.548814 |
| 1    | 2000-10-06 | id\_00     | 0.715189 |
| 2    | 2000-10-07 | id\_00     | 0.602763 |
| 3    | 2000-10-08 | id\_00     | 0.544883 |
| 4    | 2000-10-09 | id\_00     | 0.423655 |
| ...  | ...        | ...        | ...      |
| 5009 | 2001-05-17 | id\_19     | 0.288027 |
| 5010 | 2001-05-18 | id\_19     | 0.846305 |
| 5011 | 2001-05-19 | id\_19     | 0.791284 |
| 5012 | 2001-05-20 | id\_19     | 0.578636 |
| 5013 | 2001-05-21 | id\_19     | 0.288589 |

```python theme={null}
test_eq(set(prices_catalog['unique_id']), set(series_for_prices['unique_id']))
test_fail(lambda: generate_prices_for_series(series), contains='equal ends')
```

***

### `PredictionIntervals`

```python theme={null}
PredictionIntervals(n_windows=2, h=1, method='conformal_distribution')
```

Class for storing prediction intervals metadata information.