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Dates in timeseries modelsΒΆ

Link to Notebook GitHub

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.TimeSeries(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    20.003302
2006-12-31    24.703999
2007-12-31    20.026145
2008-12-31    23.473645
2009-12-31    30.858584
2010-12-31    61.335464
2011-12-31    87.024706
2012-12-31    91.321256
2013-12-31    79.921607
2014-12-31    60.799490
2015-12-31    40.374843
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)
[ 20.0033  24.704   20.0261  23.4736  30.8586  61.3355  87.0247  91.3213
  79.9216  60.7995  40.3748]

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)
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-12-31, ..., 2015-12-31]
Length: 11, Freq: A-DEC, Timezone: None

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

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