Use TSFRESH-library to forecast values

Have some issue with understanding how to use TSFERSH-library (version 0.4.0) to forecast next N-values of particular series. Below my code:

    # 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)

However, after line X = extract_features(...) I see at debugger following results

It’s mean that initial X-dataset/features (shape=(722,10) were transformed to shape (80, 1899).

Where does ’80’ come from? I guess from train.TS_ID comes. But my XT-dataset still contains 722-rows (9 days * 80 different series per day).

So, how can I predict for 9 days in advance? or is there only forecast for next period?