> ## 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.

# Sklearn models

> Use any scikit-learn model for forecasting

statsforecast supports providing scikit-learn models through the
`statsforecast.models.SklearnModel` wrapper. This can help you leverage
feature engineering and train one model per serie, which can sometimes
be better than training a single global model (as in mlforecast).

## Data setup

```python theme={null}
from functools import partial

from datasetsforecast.m4 import M4, M4Info
from sklearn.linear_model import Lasso, Ridge
from utilsforecast.feature_engineering import pipeline, trend, fourier
from utilsforecast.plotting import plot_series

from statsforecast import StatsForecast
from statsforecast.models import SklearnModel
from statsforecast.utils import ConformalIntervals
```

```python theme={null}
group = 'Hourly'
season_length = M4Info[group].seasonality
horizon = M4Info[group].horizon
data, *_ = M4.load('data', group)
data['ds'] = data['ds'].astype('int64')
valid = data.groupby('unique_id').tail(horizon).copy()
train = data.drop(valid.index)
train.head()
```

|   | unique\_id | ds | y     |
| - | ---------- | -- | ----- |
| 0 | H1         | 1  | 605.0 |
| 1 | H1         | 2  | 586.0 |
| 2 | H1         | 3  | 586.0 |
| 3 | H1         | 4  | 559.0 |
| 4 | H1         | 5  | 511.0 |

## Generating features

The utilsforecast library [provides some utilies for feature
engineering](../../../utilsforecast/feature_engineering.html).

```python theme={null}
train_features, valid_features = pipeline(
    train,
    features=[
        trend,        
        partial(fourier, season_length=season_length, k=10),  # 10 fourier terms
    ],
    freq=1,
    h=horizon,
)
train_features.head()
```

|   | unique\_id | ds | y     | trend | sin1\_24  | sin2\_24  | sin3\_24  | sin4\_24  | sin5\_24  | sin6\_24  | sin7\_24  | sin8\_24  | sin9\_24  | sin10\_24 | cos1\_24 | cos2\_24 | cos3\_24  | cos4\_24  | cos5\_24  | cos6\_24  | cos7\_24  | cos8\_24  | cos9\_24  | cos10\_24 |
| - | ---------- | -- | ----- | ----- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | -------- | -------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- |
| 0 | H1         | 1  | 605.0 | 261.0 | -0.707105 | -1.000000 | -0.707108 | -0.000012 | 0.707112  | 1.000000  | 0.707095  | 0.000024  | -0.707125 | -1.000000 | 0.707109 | 0.000006 | -0.707106 | -1.000000 | -0.707101 | -0.000003 | 0.707119  | 1.000000  | 0.707088  | -0.000015 |
| 1 | H1         | 2  | 586.0 | 262.0 | -0.500001 | -0.866027 | -1.000000 | -0.866023 | -0.499988 | -0.000007 | 0.500001  | 0.866031  | 1.000000  | 0.866011  | 0.866025 | 0.499998 | 0.000004  | -0.500005 | -0.866032 | -1.000000 | -0.866025 | -0.499991 | 0.000019  | 0.500025  |
| 2 | H1         | 3  | 586.0 | 263.0 | -0.258817 | -0.499997 | -0.707103 | -0.866021 | -0.965931 | -1.000000 | -0.965922 | -0.866033 | -0.707098 | -0.499964 | 0.965926 | 0.866027 | 0.707111  | 0.500007  | 0.258799  | 0.000012  | -0.258835 | -0.499986 | -0.707116 | -0.866046 |
| 3 | H1         | 4  | 559.0 | 264.0 | 0.000005  | 0.000011  | 0.000008  | 0.000021  | 0.000003  | 0.000016  | -0.000001 | 0.000042  | -0.000006 | 0.000007  | 1.000000 | 1.000000 | 1.000000  | 1.000000  | 1.000000  | 1.000000  | 1.000000  | 1.000000  | 1.000000  | 1.000000  |
| 4 | H1         | 5  | 511.0 | 265.0 | 0.258820  | 0.500002  | 0.707114  | 0.866027  | 0.965925  | 1.000000  | 0.965930  | 0.866022  | 0.707106  | 0.500005  | 0.965926 | 0.866024 | 0.707099  | 0.499997  | 0.258822  | -0.000021 | -0.258803 | -0.500006 | -0.707107 | -0.866022 |

## Forecasting

```python theme={null}
sf = StatsForecast(
    models=[
        SklearnModel(Lasso()),
        SklearnModel(Ridge()),
    ],
    freq=1,
)
preds = sf.forecast(
    df=train_features,
    h=horizon,
    X_df=valid_features,
    prediction_intervals=ConformalIntervals(n_windows=4, h=horizon),
    level=[95],
)
plot_series(train, preds, level=[95], palette='tab20b', max_ids=4)
```

<img src="https://mintcdn.com/nixtla/IsnpW4EtN0WT92Qq/statsforecast/docs/how-to-guides/sklearn_models_files/figure-markdown_strict/cell-6-output-1.png?fit=max&auto=format&n=IsnpW4EtN0WT92Qq&q=85&s=c1c1f61a946387c749242139e0ec3734" alt="" width="2186" height="622" data-path="statsforecast/docs/how-to-guides/sklearn_models_files/figure-markdown_strict/cell-6-output-1.png" />