 Dates in timeseries models  In [1]: from __future__ import print_function
import statsmodels.api as sm
import numpy as np
import pandas as pd
  Getting started  In [2]: data = sm.datasets.sunspots.load()
  Right now an annual date series must be datetimes at the end of the year.  In [3]: from datetime import datetime
dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))
  Using Pandas
Make a pandas TimeSeries or DataFrame  In [4]: endog = pd.Series(data.endog, index=dates)
  Instantiate the model  In [5]: ar_model = sm.tsa.AR(endog, freq='A')
pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
  Out-of-sample prediction  In [6]: pred = pandas_ar_res.predict(start='2005', end='2015')
print(pred)
      2005-12-31    20.003298
2006-12-31    24.703996
2007-12-31    20.026133
2008-12-31    23.473641
2009-12-31    30.858566
2010-12-31    61.335414
2011-12-31    87.024635
856-415-3006.321196
856-415-3006.921585
856-415-3006.799495
856-415-3006.374852
Freq: A-DEC, dtype: float64
     Using explicit dates  In [7]: ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A')
ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
pred = ar_res.predict(start='2005', end='2015')
print(pred)
      [20.00329{{SOCIAL_SECURITY_NUMBER+PHONE+SOCIAL_SECURITY_NUMBER}}3{{SOCIAL_SECURITY_NUMBER+PHONE+SOCIAL_SECURITY_NUMBER}}664  856-415-3006
 {{PHONE+SOCIAL_SECURITY_NUMBER+PHONE+SOCIAL_SECURITY_NUMBER}}8{{SOCIAL_SECURITY_NUMBER+PHONE+SOCIAL_SECURITY_NUMBER+PHONE}}]
     This just returns a regular array, but since the model has date information attached, you can get the prediction dates in a roundabout way.  In [8]: print(ar_res.data.predict_dates)
      DatetimeIndex(['2005-12-31', '2006-12-31', '2007-12-31', '2008-12-31',
               '2009-12-31', '2010-12-31', '2011-12-31', '2012-12-31',
               '2013-12-31', '2014-12-31', '2015-12-31'],
              dtype='datetime64[ns]', freq='A-DEC')
     Note: This attribute only exists if predict has been called. It holds the dates associated with the last call to predict.

    © 2009–2012 Statsmodels Developers© 2006–2008 Scipy Developers
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