Exogenous features

import lightgbm as lgb
import pandas as pd
from mlforecast import MLForecast
from mlforecast.lag_transforms import ExpandingMean, RollingMean
from mlforecast.utils import generate_daily_series, generate_prices_for_series

series = generate_daily_series(
    100, equal_ends=True, n_static_features=2
).rename(columns={'static_1': 'product_id'})
series.head()

prices_catalog = generate_prices_for_series(series)
prices_catalog.head()

series_with_prices = series.merge(prices_catalog, how='left')
series_with_prices.head()

fcst = MLForecast(
    models=lgb.LGBMRegressor(n_jobs=1, random_state=0, verbosity=-1),
    freq='D',
    lags=[7],
    lag_transforms={
        1: [ExpandingMean()],
        7: [RollingMean(window_size=14)],
    },
    date_features=['dayofweek', 'month'],
    num_threads=2,
)
fcst.fit(series_with_prices, static_features=['static_0', 'product_id'])

MLForecast(models=[LGBMRegressor], freq=D, lag_features=['lag7', 'expanding_mean_lag1', 'rolling_mean_lag7_window_size14'], date_features=['dayofweek', 'month'], num_threads=2)

fcst.ts.features_order_

['static_0',
 'product_id',
 'price',
 'lag7',
 'expanding_mean_lag1',
 'rolling_mean_lag7_window_size14',
 'dayofweek',
 'month']

preds = fcst.predict(h=7, X_df=prices_catalog)
preds.head()

from sklearn.linear_model import LinearRegression
from utilsforecast.feature_engineering import fourier

series.head()

transformed_df, future_df = fourier(series, freq='D', season_length=7, k=2, h=7)

transformed_df.head()

future_df.head()

fcst2 = MLForecast(models=LinearRegression(), freq='D')
fcst2.fit(transformed_df, static_features=['static_0', 'product_id'])

MLForecast(models=[LinearRegression], freq=D, lag_features=[], date_features=[], num_threads=1)

fcst2.predict(h=7, X_df=future_df).head()