> ## Documentation Index > Fetch the complete documentation index at: https://nixtla-feat-posthog-analytics.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Quick Start > 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](../../2_Getting_Started_complete) 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](../../0_Installation). 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](../../models): \* `models`: a list of models. Select the models you want from [models](../../models) 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. > **Note** > > StatsForecast achieves its blazing speed using JIT compiling through > Numba. The first time you call the statsforecast class, the fit method > should take around 5 seconds. The second time -once Numba compiled > your settings- it should take less than 0.2s. ```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]) ``` > **Next Steps** > > * Build and end-to-end forecasting pipeline following best practices > in [End to End Walkthrough](./2_Getting_Started_complete) > * [Forecast millions of series](../experiments/Prophet_spark_m5) in > a scalable cluster in the cloud using Spark and Nixtla > * [Detect anomalies](../tutorials/AnomalyDetection) in your past > observations