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如何使用 Glunts DeepAR预测未知的未来目标值？我有一个从 1995-01-01 到 2021-10-01 的时间序列。每月频率数据。如何预测未来（未来 3 个月）的价值：2021-11-01 至 2022-01-01？请注意，我没有 2021-11-01、2021-12-01 和 2022-01-01 的目标值。非常感谢！

from gluonts.model.deepar import DeepAREstimator
from gluonts.mx import Trainer 
import numpy as np
import mxnet as mx

np.random.seed(7)
mx.random.seed(7)

estimator = DeepAREstimator(
    prediction_length=12
    , context_length=120
    , freq='M'
    , trainer=Trainer(        
        epochs=5
        , learning_rate=1e-03
        , num_batches_per_epoch=50))

predictor = estimator.train(training_data=df_train)

# Forecasting
predictions = predictor.predict(df_test)
predictions = list(predictions)[0]
predictions = predictions.quantile(0.5)

print(predictions)
[163842.34  152805.08  161326.3   176823.97  127003.79  126937.78
 139575.2   117121.67  115754.67  139211.28  122623.586 120102.65 ]

据我了解，预测值不适用于“2021-11-01”、“2021-12-01”和“2022-01-01”。我怎么知道这个值是指哪个月份？如何预测未来 3 个月的价值：“2021-11-01”、“2021-12-01”和“2022-01-01”？

看看这段代码。它来自“使用 Python 进行高级预测”。 https://github.com/Apress/advanced-forecasting-python/blob/main/Chapter%2020%20-%20Amazon's%20DeepAR.ipynb

一旦将 test_ds 的最后 28 个值（清单 20-5.R2 分数和预测图）与在同一数据集 test_ds 上做出的预测（清单 20-4.预测）进行比较，它似乎不会预测未知的未来值

如何预测未知的未来价值？

非常感谢！

数据源

https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting

# Listing 20-1. Importing the data
import pandas as pd
y = pd.read_csv('air_visit_data.csv.zip')
y = y.pivot(index='visit_date', columns='air_store_id')['visitors']
y = y.fillna(0)
y = pd.DataFrame(y.sum(axis=1))

y = y.reset_index(drop=False)
y.columns = ['date', 'y']


# Listing 20-2. Preparing the data format requered by the gluonts library
from gluonts.dataset.common import ListDataset
start = pd.Timestamp("01-01-2016", freq="H")
# train dataset: cut the last window of length "prediction_length", add "target" and "start" fields
train_ds = ListDataset([{'target': y.loc[:450,'y'], 'start': start}], freq='H')
# test dataset: use the whole dataset, add "target" and "start" fields
test_ds = ListDataset([{'target': y['y'], 'start': start}],freq='H')


# Listing 20-3. Fitting the default DeepAR model
from gluonts.model.deepar import DeepAREstimator
from gluonts.trainer import Trainer
import mxnet as mx
import numpy as np

np.random.seed(7)
mx.random.seed(7)

estimator = DeepAREstimator(
    prediction_length=28,
    context_length=100,
    freq=’H’,
    trainer=Trainer(ctx="gpu", # remove if running on windows
                    epochs=5,
                    learning_rate=1e-3,
                    num_batches_per_epoch=100
                   )
)

predictor = estimator.train(train_ds)



# Listing 20-4. Prediction
predictions = predictor.predict(test_ds)
predictions = list(predictions)[0]
predictions = predictions.quantile(0.5)



# Listing 20-5. R2 score and prediction graph
from sklearn.metrics import r2_score
print(r2_score( list(test_ds)[0]['target'][-28:], predictions))

import matplotlib.pyplot as plt
plt.plot(predictions)
plt.plot(list(test_ds)[0]['target'][-28:])
plt.legend(['predictions', 'actuals'])
plt.show()
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