Dates in timeseries models ============================ .. _tsa_dates_notebook: `Link to Notebook GitHub `_ .. raw:: html

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from __future__ import print_function
   import statsmodels.api as sm
   import numpy as np
   import pandas as pd

Getting started¶

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data = sm.datasets.sunspots.load()

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

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

Using Pandas¶

Make a pandas TimeSeries or DataFrame

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

Instantiate the model

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

Out-of-sample prediction

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

Using explicit dates¶

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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)

2005-12-31    20.003285
   2006-12-31    24.703979
   2007-12-31    20.026123
   2008-12-31    23.473638
   2009-12-31    30.858572
   2010-12-31    61.335449
   2011-12-31    87.024691
   2012-12-31    91.321256
   2013-12-31    79.921629
   2014-12-31    60.799526
   2015-12-31    40.374879
   Freq: A-DEC, dtype: float64

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

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print(ar_res.data.predict_dates)

[ 20.00328112  24.70398853  20.02613031  23.47364995  30.85857026
     61.33544403  87.02467843  91.32123263  79.92159878  60.79948588
     40.37483539]

Note: This attribute only exists if predict has been called. It holds the dates associated with the last call to predict.