skforecast/skforecast-ai/forecasting-multiple-series

import pandas as pd
from lightgbm import LGBMRegressor
from skforecast.recursive import ForecasterRecursiveMultiSeries
from skforecast.model_selection import backtesting_forecaster_multiseries, TimeSeriesFold
# 1. Load data as wide DataFrame (columns = series)
series = pd.read_csv('data.csv', index_col='date', parse_dates=True)
series = series.asfreq('D')
# 2. Create forecaster
forecaster = ForecasterRecursiveMultiSeries(
    estimator=LGBMRegressor(n_estimators=200, random_state=123),
    lags=24,
    encoding='ordinal',       # 'ordinal', 'ordinal_category', 'onehot', or None
    transformer_series=None,  # Apply same transformer to all series
    # Per-series options (dict):
    # transformer_series={'series_1': StandardScaler(), 'series_2': MinMaxScaler()},
    # weight_func={'series_1': custom_weights_fn, '_default': None},
    # differentiation={'series_1': 1, 'series_2': None},
    categorical_features='auto',  # Auto-detect and encode non-numeric exog columns
    differentiation=None,
    dropna_from_series=False,     # True to drop NaN rows; False to keep (NaN-tolerant estimators)
)
# 3. Train
forecaster.fit(series=series)
# 4. Predict all series
predictions = forecaster.predict(steps=10)
# 5. Predict specific series only
predictions = forecaster.predict(steps=10, levels=['series_1', 'series_2'])
# 6. Backtesting (multi-series)
cv = TimeSeriesFold(
    steps=10,
    initial_train_size=len(series) - 100,
    refit=False,
)
metric, predictions_bt = backtesting_forecaster_multiseries(
    forecaster=forecaster,
    series=series,
    cv=cv,
    metric='mean_absolute_error',
    levels=None,  # Evaluate all series
)
print(metric)  # Shows per-series and aggregated metrics

# Exog can also be wide DataFrame, long DataFrame, or dict
forecaster.fit(series=series, exog=exog_df)
predictions = forecaster.predict(steps=10, exog=exog_test)

from skforecast.direct import ForecasterDirectMultiVariate
# Predicts ONE target series using lags from ALL series as features
# Note: transformer_series defaults to StandardScaler() (unlike other forecasters)
forecaster = ForecasterDirectMultiVariate(
    level='target_series',    # Name of the series to predict
    steps=10,
    estimator=LGBMRegressor(n_estimators=100, random_state=123),
    lags=24,                  # Or dict: {'series_a': 12, 'series_b': 24}
    transformer_series=StandardScaler(),  # Default — set None to disable scaling
    categorical_features='auto',  # Auto-detect and encode non-numeric exog columns
    dropna_from_series=False,     # True to drop NaN rows; False to keep (NaN-tolerant estimators)
)
forecaster.fit(series=series_df)
predictions = forecaster.predict()

from skforecast.preprocessing import (
    reshape_series_wide_to_long,
    reshape_series_long_to_dict,
    reshape_exog_long_to_dict,
    reshape_series_exog_dict_to_long,
)
# Wide → Long
series_long = reshape_series_wide_to_long(series_wide)
# Long → Dict (freq is required)
series_dict = reshape_series_long_to_dict(series_long, freq='D')
exog_dict = reshape_exog_long_to_dict(exog_long, freq='D')