Dates in timeseries models

import statsmodels.api as sm
import numpy as np
import pandas as pd

Getting started

data = sm.datasets.sunspots.load()

Right now an annual date series must be datetimes at the end of the year.

from datetime import datetime
dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))

Using Pandas

Make a pandas TimeSeries or DataFrame

endog = pd.Series(data.endog, index=dates)

Instantiate the model

ar_model = sm.tsa.AR(endog, freq='A')
pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)

/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/ar_model.py:691: FutureWarning:
statsmodels.tsa.AR has been deprecated in favor of statsmodels.tsa.AutoReg and
statsmodels.tsa.SARIMAX.

AutoReg adds the ability to specify exogenous variables, include time trends,
and add seasonal dummies. The AutoReg API differs from AR since the model is
treated as immutable, and so the entire specification including the lag
length must be specified when creating the model. This change is too
substantial to incorporate into the existing AR api. The function
ar_select_order performs lag length selection for AutoReg models.

AutoReg only estimates parameters using conditional MLE (OLS). Use SARIMAX to
estimate ARX and related models using full MLE via the Kalman Filter.

To silence this warning and continue using AR until it is removed, use:

import warnings
warnings.filterwarnings('ignore', 'statsmodels.tsa.ar_model.AR', FutureWarning)

  warnings.warn(AR_DEPRECATION_WARN, FutureWarning)

Out-of-sample prediction

pred = pandas_ar_res.predict(start='2005', end='2015')
print(pred)

2005-12-31    20.003293
2006-12-31    24.703980
2007-12-31    20.026125
2008-12-31    23.473639
2009-12-31    30.858582
2010-12-31    61.335455
2011-12-31    87.024687
2012-12-31    91.321234
2013-12-31    79.921599
2014-12-31    60.799502
2015-12-31    40.374871
Freq: A-DEC, dtype: float64

Using explicit dates

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)

[20.00329308 24.70398036 20.02612492 23.47363929 30.85858227 61.33545516
 87.02468683 91.3212336  79.92159926 60.79950228 40.37487059]

This just returns a regular array, but since the model has date information attached, you can get the prediction dates in a roundabout way.

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.