Sklearn models - Nixtla

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

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

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.

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

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)

Related topics

Models Probabilistic forecasting | MLForecast Target transforms Target transformations Machine Learning 🤖 Forecast