Dates in timeseries models

[1]:
import statsmodels.api as sm
import numpy as np
import pandas as pd

Getting started

[2]:
data = sm.datasets.sunspots.load()

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

[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

[4]:
endog = pd.Series(data.endog, index=dates)

Instantiate the model

[5]:
ar_model = sm.tsa.AR(endog, freq='A')
pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
/usr/lib/python3/dist-packages/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

[6]:
pred = pandas_ar_res.predict(start='2005', end='2015')
print(pred)
2005-12-31    20.003273
2006-12-31    24.703967
2007-12-31    20.026117
2008-12-31    23.473641
2009-12-31    30.858584
2010-12-31    61.335485
2011-12-31    87.024743
2012-12-31    91.321311
2013-12-31    79.921675
2014-12-31    60.799558
2015-12-31    40.374895
Freq: A-DEC, dtype: float64

Using explicit dates

[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)
[20.00327326 24.70396672 20.02611743 23.47364104 30.85858443 61.33548464
 87.02474275 91.32131078 79.92167534 60.79955839 40.37489459]

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

[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.