Using dates with timeseries models



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

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 = pandas.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

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

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