在理解如何使用 TSFESH 库（版本 0.4.0）来预测特定系列的下一个 N 值方面存在一些问题。在我的代码下面：

    # load data train/test datasets        
    train, Y, test, YY = prepare_train_test()
    # add series ID         
    train['TS_ID'] = pd.Categorical(train['QTR_HR_START']).codes
    test['TS_ID'] = pd.Categorical(test['QTR_HR_START']).codes
    # add ordered id for concrete event of series
    for id in sorted(train['TS_ID'].unique()):
        train.ix[train.TS_ID == id, 'TIME_ORDER_ID'] =  pd.Categorical(train[train.TS_ID == id]['DATETIME']).codes
    for id in sorted(test['TS_ID'].unique()):
        test.ix[test.TS_ID == id, 'TIME_ORDER_ID'] = pd.Categorical(test[test.TS_ID == id]['DATETIME']).codes
    # perform feature extraction for my signal
    extraction_settings = FeatureExtractionSettings()
    extraction_settings.IMPUTE = impute  # Fill in Infs and NaNs
    X = extract_features(train, column_id='TS_ID', feature_extraction_settings=extraction_settings).values
    XT = extract_features(test, column_id='TS_ID', feature_extraction_settings=extraction_settings).values

    # there should be as example 
    # model = xgb.DMatrix(X, label=Y, missing=np.nan)
    # model.fit()
    # model.predict(XT)

但是，在X = extract_features(...)我在调试器中看到以下结果之后

这意味着初始X-dataset/features(shape=(722,10) 被转换为形状 (80, 1899)。

“80”从何而来？我猜train.TS_ID来自。但我的XT-dataset 仍然包含 722 行（9 天 * 每天 80 个不同的系列）。

那么，如何提前9天预测呢？还是只有下一个时期的预测？