用于样本外预测的ARMA.predict不适用于浮点数？

在我开发了用于样本内分析的小型ARMAX预测模型之后，我想预测样本中的一些数据 .

我用于预测计算的时间序列从2013-01-01开始，到2013-12-31结束！

这是我正在使用的数据：

hr = np.loadtxt("Data_2013_17.txt")
index = date_range(start='2013-1-1', end='2013-12-31', freq='D')
df = pd.DataFrame(hr, index=index)
holidays = ['2013-1-1', '2013-3-29', '2013-4-1', '2013-5-1', '2013-5-9', '2013-5-20', '2013-10-3', '2013-12-25', '2013-12-26']
# holidays for all Bundesländer 
idx = df.asfreq('B').index - DatetimeIndex(holidays)
indexed_df = df.reindex(idx)
# indexed_df = df.asfreq('B') (includes holidays)
# 'D'=day
#'B'=business day
# W@MON=shows only mondays

# external variable  
hr_ = np.loadtxt("Data_2_2013.txt")
index = date_range(start='2013-1-1', end='2013-12-31', freq='D')
df = pd.DataFrame(hr_, index=index)
idx2 = df.asfreq('B').index - DatetimeIndex(holidays)
external_df1 = df.reindex(idx2)
external_df = external_df1.fillna(external_df1.mean())

日期：

0
2013-01-02  49.56
2013-01-03  48.09
2013-01-04  36.79
2013-01-07  60.84
2013-01-08  59.72
2013-01-09  61.88
2013-01-10  57.95
2013-01-11  56.29
2013-01-14  57.89
2013-01-15  64.49
2013-01-16  58.92
2013-01-17  62.30
2013-01-18  55.92
2013-01-21  55.67
2013-01-22  60.73
2013-01-23  60.12
2013-01-24  65.70
2013-01-25  55.15
2013-01-28  51.79
2013-01-29  39.69
2013-01-30  37.90
2013-01-31  37.60
2013-02-01  41.26
2013-02-04  29.18
2013-02-05  39.55
2013-02-06  47.57
2013-02-07  51.97
2013-02-08  46.95
2013-02-11  42.79
2013-02-12  51.83
...           ...
2013-11-18  58.04
2013-11-19  62.96
2013-11-20  63.90
2013-11-21  64.09
2013-11-22  64.78
2013-11-25  59.59
2013-11-26  70.69
2013-11-27  61.57
2013-11-28  47.87
2013-11-29  34.61
2013-12-02  68.77
2013-12-03  77.84
2013-12-04  63.09
2013-12-05  40.94
2013-12-06  38.60
2013-12-09  65.79
2013-12-10  68.98
2013-12-11  77.86
2013-12-12  76.44
2013-12-13  85.90
2013-12-16  53.51
2013-12-17  73.67
2013-12-18  59.76
2013-12-19  53.11
2013-12-20  38.33
2013-12-23  36.93
2013-12-24  11.30
2013-12-27  30.32
2013-12-30  39.94
2013-12-31  31.27

[252 rows x 1 columns]
                0
2013-01-02  70770
2013-01-03  74155
2013-01-04  74286
2013-01-07  75360
2013-01-08  76910
2013-01-09  78561
2013-01-10  77427
2013-01-11  75260
2013-01-14  78738
2013-01-15  78286
2013-01-16  79568
2013-01-17  79761
2013-01-18  77518
2013-01-21  80089
2013-01-22  79915
2013-01-23  78607
2013-01-24  79761
2013-01-25  77908
2013-01-28  79873
2013-01-29  80535
2013-01-30  76340
2013-01-31  78244
2013-02-01  77749
2013-02-04  79125
2013-02-05  79001
2013-02-06  77837
2013-02-07  77495
2013-02-08  75372
2013-02-11  73856
2013-02-12  77494
...           ...
2013-11-18  76292
2013-11-19  77420
2013-11-20  74993
2013-11-21  76658
2013-11-22  74769
2013-11-25  78347
2013-11-26  77756
2013-11-27  79648
2013-11-28  80075
2013-11-29  78587
2013-12-02  76867
2013-12-03  76070
2013-12-04  80344
2013-12-05  81736
2013-12-06  79617
2013-12-09  78085
2013-12-10  78430
2013-12-11  78120
2013-12-12  77735
2013-12-13  75872
2013-12-16  78651
2013-12-17  76180
2013-12-18  75867
2013-12-19  76018
2013-12-20  71101
2013-12-23  66841
2013-12-24  64557
2013-12-27  66747
2013-12-30  64787
2013-12-31  61101

[252 rows x 1 columns]

Descriptive statistics of ts:
                0
count  252.000000
mean    44.583651
std     11.708938
min     11.300000
25%     34.597500
50%     44.200000
75%     51.947500
max     85.900000

Skewness of endog_var: [ 0.44315988]

Kurtsosis of endog_var: [ 3.18049689]

Correlation hr & hr_: (0.71074420030220553, 2.0635001219278823e-57)

Augmented Dickey-Fuller Test for endog_var: (-2.9282259926181839, 0.042162780619902182, {'5%': -2.8698573654386559, '1%': -3.4492269328800189, '10%': -2.5712010851306641}, <statsmodels.tsa.stattools.ResultsStore object at 0x111e2ca50>)

选择p和q值：

在：arma_mod = sm.tsa.ARMA（indexed_df，（3,3），external_df）.fit（）z = arma_mod.params print'P-和Q-values：'print z

日期：

P- and Q-Values:
const      19.674538
0           0.000345
ar.L1.0    -0.062796
ar.L2.0     0.340800
ar.L3.0     0.436345
ma.L1.0     0.613498
ma.L2.0     0.057267
ma.L3.0    -0.415455
dtype: float64
/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  "Check mle_retvals", ConvergenceWarning)

这是我做的预测样本：

在：

start_pred = '2014-1-3'
end_pred = '2014-1-3'

predict_price1 = arma_mod1.predict(start_pred, end_pred, external_df)#, dynamic=True) 
print ('Predicted Price (ARMAX): {}' .format(predict_price1))

日期：

Traceback (most recent call last):

  File "<ipython-input-34-ad7feec95e4a>", line 6, in <module>
    predict_price1 = arma_mod1.predict(start_pred, end_pred, external_df)#, dynamic=True)

  File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/base/wrapper.py", line 92, in wrapper
    return data.wrap_output(func(results, *args, **kwargs), how)

  File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 1441, in predict
    return self.model.predict(self.params, start, end, exog, dynamic)

  File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 711, in predict
    start = self._get_predict_start(start, dynamic)

  File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 646, in _get_predict_start
    method)

  File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 376, in _validate
    start = _index_date(start, dates)

  File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/base/datetools.py", line 57, in _index_date
    "an integer" % date)

ValueError: There is no frequency for these dates and date 2014-01-03 00:00:00 is not in dates index. Try giving a date that is in the dates index or use an integer

我不理解这个错误！

arima源代码，即'datetools.py'告诉我以下内容：

except KeyError as err:
        freq = _infer_freq(dates)
        if freq is None:
            #TODO: try to intelligently roll forward onto a date in the
            # index. Waiting to drop pandas 0.7.x support so this is
            # cleaner to do.
            raise ValueError("There is no frequency for these dates and "
                             "date %s is not in dates index. Try giving a "
                             "date that is in the dates index or use "
                             "an integer" % date)

        # we can start prediction at the end of endog
        if _idx_from_dates(dates[-1], date, freq) == 1:
            return len(dates)

        raise ValueError("date %s not in date index. Try giving a "
                         "date that is in the dates index or use an integer"
                         % date)

def _date_from_idx(d1, idx, freq):
    """
    Returns the date from an index beyond the end of a date series.
    d1 is the datetime of the last date in the series. idx is the
    index distance of how far the next date should be from d1. Ie., 1 gives
    the next date from d1 at freq.

    Notes
    -----
    This does not do any rounding to make sure that d1 is actually on the
    offset. For now, this needs to be taken care of before you get here.
    """

这意味着应该可以预测样本 . 我只是不明白我需要在哪里以及如何更改我的对象？！

我发现了一些较旧的帖子，但他们不会告诉我该怎么做：Python out of sample forecasting ARIMA predict()和https://stats.stackexchange.com/questions/76160/im-not-sure-that-statsmodels-is-predicting-out-of-sample

如何使用上面给出的信息预测样本数据？

非常感谢