每一步一个模型

import random
import lightgbm as lgb
import pandas as pd
from datasetsforecast.m4 import M4, M4Info
from utilsforecast.evaluation import evaluate
from utilsforecast.losses import smape

from mlforecast import MLForecast
from mlforecast.lag_transforms import ExponentiallyWeightedMean, RollingMean
from mlforecast.target_transforms import Differences

group = 'Hourly'
await M4.async_download('data', group=group)
df, *_ = M4.load(directory='data', group=group)
df['ds'] = df['ds'].astype('int')
ids = df['unique_id'].unique()
random.seed(0)
sample_ids = random.choices(ids, k=4)
sample_df = df[df['unique_id'].isin(sample_ids)]
info = M4Info[group]
horizon = info.horizon
valid = sample_df.groupby('unique_id').tail(horizon)
train = sample_df.drop(valid.index)

def avg_smape(df):
    """Computes the SMAPE by serie and then averages it across all series."""
    full = df.merge(valid)
    return (
        evaluate(full, metrics=[smape])
        .drop(columns='metric')
        .set_index('unique_id')
        .squeeze()
    )

fcst = MLForecast(
    models=lgb.LGBMRegressor(random_state=0, verbosity=-1),
    freq=1,
    lags=[24 * (i+1) for i in range(7)],
    lag_transforms={
        1: [RollingMean(window_size=24)],
        24: [RollingMean(window_size=24)],
        48: [ExponentiallyWeightedMean(alpha=0.3)],
    },
    num_threads=1,
    target_transforms=[Differences([24])],
)

horizon = 24
# the following will train 24 models, one for each horizon
individual_fcst = fcst.fit(train, max_horizon=horizon)
individual_preds = individual_fcst.predict(horizon)
avg_smape_individual = avg_smape(individual_preds).rename('individual')
# the following will train a single model and use the recursive strategy
recursive_fcst = fcst.fit(train)
recursive_preds = recursive_fcst.predict(horizon)
avg_smape_recursive = avg_smape(recursive_preds).rename('recursive')
# results
print('Average SMAPE per method and serie')
avg_smape_individual.to_frame().join(avg_smape_recursive).applymap('{:.1%}'.format)

Average SMAPE per method and serie