> ## 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.

# Quick Start | StatsForecast

> Minimal Example of StatsForecast

`StatsForecast` follows the sklearn model API. For this minimal example,
you will create an instance of the StatsForecast class and then call its
`fit` and `predict` methods. We recommend this option if speed is not
paramount and you want to explore the fitted values and parameters.

> **Tip**
>
> If you want to forecast many series, we recommend using the `forecast`
> method. Check this [Getting Started with multiple time
> series](./getting_started_complete.html) guide.

The input to StatsForecast is always a data frame in [long
format](https://www.theanalysisfactor.com/wide-and-long-data/) with
three columns: `unique_id`, `ds` and `y`:

* The `unique_id` (string, int or category) represents an identifier
  for the series.

* The `ds` (datestamp) column should be of a format expected by
  Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a
  timestamp.

* The `y` (numeric) represents the measurement we wish to forecast.

As an example, let’s look at the US Air Passengers dataset. This time
series consists of monthly totals of a US airline passengers from 1949
to 1960. The CSV is available
[here](https://www.kaggle.com/datasets/chirag19/air-passengers).

We assume you have StatsForecast already installed. Check this guide for
instructions on [how to install StatsForecast](./installation.html).

First, we’ll import the data:

```python theme={null}
# uncomment the following line to install the library
# %pip install statsforecast
```

```python theme={null}
import pandas as pd
```

```python theme={null}
df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/air-passengers.csv', parse_dates=['ds'])
df.head()
```

|   | unique\_id    | ds         | y   |
| - | ------------- | ---------- | --- |
| 0 | AirPassengers | 1949-01-01 | 112 |
| 1 | AirPassengers | 1949-02-01 | 118 |
| 2 | AirPassengers | 1949-03-01 | 132 |
| 3 | AirPassengers | 1949-04-01 | 129 |
| 4 | AirPassengers | 1949-05-01 | 121 |

We fit the model by instantiating a new `StatsForecast` object with its
[two required parameters](../../src/core/models.html): \* `models`: a
list of models. Select the models you want from
[models](../../src/core/models.html) and import them. For this example,
we will use a `AutoARIMA` model. We set `season_length` to 12 because we
expect seasonal effects every 12 months. (See: [Seasonal
periods](https://robjhyndman.com/hyndsight/seasonal-periods/))

* `freq`: a string indicating the frequency of the data. (See [pandas
  available
  frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)

Any settings are passed into the constructor. Then you call its fit
method and pass in the historical data frame.

```python theme={null}
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
```

```python theme={null}
sf = StatsForecast(
    models=[AutoARIMA(season_length = 12)],
    freq='MS',
)
sf.fit(df)
```

```text theme={null}
StatsForecast(models=[AutoARIMA])
```

The `predict` method takes two arguments: forecasts the next `h` (for
horizon) and `level`.

* `h` (int): represents the forecast h steps into the future. In this
  case, 12 months ahead.

* `level` (list of floats): this optional parameter is used for
  probabilistic forecasting. Set the `level` (or confidence
  percentile) of your prediction interval. For example, `level=[90]`
  means that the model expects the real value to be inside that
  interval 90% of the times.

The forecast object here is a new data frame that includes a column with
the name of the model and the y hat values, as well as columns for the
uncertainty intervals.

```python theme={null}
forecast_df = sf.predict(h=12, level=[90])
forecast_df.tail()
```

|    | unique\_id    | ds         | AutoARIMA  | AutoARIMA-lo-90 | AutoARIMA-hi-90 |
| -- | ------------- | ---------- | ---------- | --------------- | --------------- |
| 7  | AirPassengers | 1961-08-01 | 633.236389 | 590.009033      | 676.463745      |
| 8  | AirPassengers | 1961-09-01 | 535.236389 | 489.558899      | 580.913940      |
| 9  | AirPassengers | 1961-10-01 | 488.236389 | 440.233795      | 536.239014      |
| 10 | AirPassengers | 1961-11-01 | 417.236389 | 367.016205      | 467.456604      |
| 11 | AirPassengers | 1961-12-01 | 459.236389 | 406.892456      | 511.580322      |

You can plot the forecast by calling the `StatsForecast.plot` method and
passing in your forecast dataframe.

```python theme={null}
sf.plot(df, forecast_df, level=[90])
```

<img src="https://mintcdn.com/nixtla/IsnpW4EtN0WT92Qq/statsforecast/docs/getting-started/getting_Started_short_files/figure-markdown_strict/cell-8-output-1.png?fit=max&auto=format&n=IsnpW4EtN0WT92Qq&q=85&s=46a30b103df4b5069ef6b1ef70085356" alt="" width="1830" height="361" data-path="statsforecast/docs/getting-started/getting_Started_short_files/figure-markdown_strict/cell-8-output-1.png" />

> **Next Steps**
>
> * Build and end-to-end forecasting pipeline following best practices
>   in [End to End Walkthrough](./getting_started_complete.html)
> * [Forecast millions of
>   series](../experiments/prophet_spark_m5.html) in a scalable
>   cluster in the cloud using Spark and Nixtla
> * [Detect anomalies](../tutorials/anomalydetection.html) in your
>   past observations