如何使用Nixtla计算训练集（样本内）的Conformal预测区间？

翻遍Nixtla官方文档，都没找到计算样本内（训练数据）预测区间的方法，目前只能实现未来时段的预测区间计算。

当前未来预测的代码示例 #

from statsforecast.models import SeasonalExponentialSmoothing, ADIDA, ARIMA
from statsforecast.utils import ConformalIntervals

# 创建模型列表及实例化参数
intervals = ConformalIntervals(h=24, n_windows=2)

models = [
    SeasonalExponentialSmoothing(season_length=24, alpha=0.1, prediction_intervals=intervals),
    ADIDA(prediction_intervals=intervals),
    ARIMA(order=(24,0,12), season_length=24, prediction_intervals=intervals),
]

sf = StatsForecast(
    df=train, 
    models=models, 
    freq='H', 
)

levels = [80, 90] # 预测区间的置信水平

forecasts = sf.forecast(h=24, level=levels)
forecasts = forecasts.reset_index()
forecasts.head()

期望实现的调用方式 #

希望能实现类似以下的调用，直接传入目标数据集df_x就能获取对应训练集部分的预测区间：

forecasts = sf.forecast(df_x, level=levels)

内容的提问来源于stack exchange，提问作者PeCaDe