import pandas as pd
import numpy as np
from utilsforecast.feature_engineering import trend
from mlforecast import MLForecast
from sklearn.linear_model import LinearRegression

# sample data
data = pd.read_csv('<https://datasets-nixtla.s3.amazonaws.com/air-passengers.csv>', parse_dates=['ds'])
h = 60

# generate features
train, future = trend(data, freq='MS', h=h)

models ={
    'Linear': LinearRegression()
}
# training
fcst = MLForecast(
    models=models,
    freq='MS',
)
fcst.fit(train, static_features=[], fitted=True)

crossvalidation_df = fcst.cross_validation(
    df=train,
    h=60,
    n_windows=1,
    refit=False,
)
crossvalidation_df.head()