这里有点新,但试图使用statsmodel ARMA预测工具.我从雅虎导入了一些股票数据并得到ARMA给我适合的参数.但是,当我使用预测代码时,我收到的是一个错误列表,我似乎无法弄清楚.不太确定我在这里做错了什么：
import pandas
import statsmodels.tsa.api as tsa
from pandas.io.data import DataReader
start = pandas.datetime(2013,1,1)
end = pandas.datetime.today()
data = DataReader('GOOG','yahoo')
arma =tsa.ARMA(data['Close'],order =(2,2))
results= arma.fit()
results.predict(start=start,end=end)
错误是：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
C:\Windows\system32\kwargs)
88 results = object.__getattribute__(self,'_results')
89 data = results.model.data
---> 90 return data.wrap_output(func(results,**kwargs),how)
91
92 argspec = inspect.getargspec(func)
D:\Python27\lib\site-packages\statsmodels-0.5.0-py2.7.egg\statsmodels\tsa\arima_
model.pyc in predict(self,start,end,exog,dynamic)
1265
1266 """
-> 1267 return self.model.predict(self.params,dynamic
)
1268
1269 def forecast(self,steps=1,exog=None,alpha=.05):
D:\Python27\lib\site-packages\statsmodels-0.5.0-py2.7.egg\statsmodels\tsa\arima_
model.pyc in predict(self,params,dynamic)
497
498 # will return an index of a date
--> 499 start = self._get_predict_start(start,dynamic)
500 end,out_of_sample = self._get_predict_end(end,dynamic)
501 if out_of_sample and (exog is None and self.k_exog > 0):
D:\Python27\lib\site-packages\statsmodels-0.5.0-py2.7.egg\statsmodels\tsa\arima_
model.pyc in _get_predict_start(self,dynamic)
404 #elif 'mle' not in method or dynamic: # should be on a date
405 start = _validate(start,k_ar,k_diff,self.data.dates,--> 406 method)
407 start = super(ARMA,self)._get_predict_start(start)
408 _check_arima_start(start,method,dynamic)
D:\Python27\lib\site-packages\statsmodels-0.5.0-py2.7.egg\statsmodels\tsa\arima_
model.pyc in _validate(start,dates,method)
160 if isinstance(start,(basestring,datetime)):
161 start_date = start
--> 162 start = _index_date(start,dates)
163 start -= k_diff
164 if 'mle' not in method and start < k_ar - k_diff:
D:\Python27\lib\site-packages\statsmodels-0.5.0-py2.7.egg\statsmodels\tsa\base\d
atetools.pyc in _index_date(date,dates)
37 freq = _infer_freq(dates)
38 # we can start prediction at the end of endog
---> 39 if _idx_from_dates(dates[-1],date,freq) == 1:
40 return len(dates)
41
D:\Python27\lib\site-packages\statsmodels-0.5.0-py2.7.egg\statsmodels\tsa\base\d
atetools.pyc in _idx_from_dates(d1,d2,freq)
70 from pandas import DatetimeIndex
71 return len(DatetimeIndex(start=d1,end=d2,---> 72 freq = _freq_to_pandas[freq])) - 1
73 except ImportError,err:
74 from pandas import Daterange
D:\Python27\lib\site-packages\statsmodels-0.5.0-py2.7.egg\statsmodels\tsa\base\d
atetools.pyc in __getitem__(self,key)
11 # being lazy,don't want to replace dictionary below
12 def __getitem__(self,key):
---> 13 return get_offset(key)
14 _freq_to_pandas = _freq_to_pandas_class()
15 except ImportError,err:
D:\Python27\lib\site-packages\pandas\tseries\frequencies.pyc in get_offset(name)
484 """
485 if name not in _dont_uppercase:
--> 486 name = name.upper()
487
488 if name in _rule_aliases:
AttributeError: 'nonetype' object has no attribute 'upper'
最佳答案
对我来说看起来像个错误.我会调查一下.
https://github.com/statsmodels/statsmodels/issues/712
编辑：作为一种解决方法,您可以从DataFrame中删除DatetimeIndex并将其传递给numpy数组.它使得预测在日期方面变得有点棘手,但是当没有频率时使用日期进行预测已经相当棘手,因此只有开始和结束日期基本上没有意义.
import pandas
import statsmodels.tsa.api as tsa
from pandas.io.data import DataReader
import pandas
data = DataReader('GOOG','yahoo')
dates = data.index
# start at a date on the index
start = dates.get_loc(pandas.datetools.parse("1-2-2013"))
end = start + 30 # "steps"
# NOTE THE .values
arma =tsa.ARMA(data['Close'].values,2))
results= arma.fit()
results.predict(start,end)
