MLForecast - Support historic covariates out of the box about mlforecast HOT 3 CLOSED

import pandas as pd
from mlforecast import MLForecast
from mlforecast.utils import generate_series, generate_prices_for_series
from sklearn.linear_model import LinearRegression

# generate data. the prices span the full range of training dates (so it's a future exog)
series = generate_series(2, equal_ends=True)
prices = generate_prices_for_series(series, horizon=0)

# turn to historic
prices_lag5 = prices.rename(columns={'price': 'price_lag5'})
prices_lag5['ds'] += 5 * pd.offsets.Day()
prices_lag10 = prices.rename(columns={'price': 'price_lag10'})
prices_lag10['ds'] += 10 * pd.offsets.Day()
historic_prices = prices_lag5.merge(prices_lag10, on=['unique_id', 'ds'])

# merge with training set. this drops some rows but if you have more history you wouldn't need to
train = series.merge(historic_prices, on=['unique_id', 'ds'])

# use the regular API for training and forecasting
mlf = MLForecast(
    models=[LinearRegression()],
    freq='D',
)
mlf.fit(train, static_features=[])
mlf.predict(h=5, X_df=historic_prices)