I would like to use pandas OLS function to fit a trendline to my data Series. Does anyone knows how to use the datetime index from the pandas Series as predictor in the OLS?
For example, let say that I have a simple time series:
>>> ts
2001-12-31 19.828763
2002-12-31 20.112191
2003-12-31 19.509116
2004-12-31 19.913656
2005-12-31 19.701649
2006-12-31 20.022819
2007-12-31 20.103024
2008-12-31 20.132712
2009-12-31 19.850609
2010-12-31 19.290640
2011-12-31 19.936210
2012-12-31 19.664813
Freq: A-DEC
I would like to do an OLS on it using the index as predictor:
model = pd.ols(y=ts,x=ts.index,intercept=True)
But as x is a list of datetime index, the function returns an error. Anyone has an idea?
I could use linregress from scipy.stats but I wonder if it is possible with Pandas.
Thanks, Greg
The problem is that you cannot pass an Index
to ols
.
Change it to a Series
:
In [153]: ts
Out[153]:
2011-01-01 00:00:00 19.828763
2011-01-01 01:00:00 20.112191
2011-01-01 02:00:00 19.509116
Freq: H, Name: 1
In [158]: type(ts.index)
Out[158]: pandas.tseries.index.DatetimeIndex
In [154]: df = ts.reset_index()
In [155]: df
Out[155]:
index 1
0 2011-01-01 00:00:00 19.828763
1 2011-01-01 01:00:00 20.112191
2 2011-01-01 02:00:00 19.509116
In [160]: type(df['index'])
Out[160]: pandas.core.series.Series
In [156]: model = pd.ols(y=df[1], x=df['index'], intercept=True)
In [163]: model
Out[163]:
-------------------------Summary of Regression Analysis-------------------------
Formula: Y ~ <x> + <intercept>
Number of Observations: 3
Number of Degrees of Freedom: 1
R-squared: -0.0002
Adj R-squared: -0.0002
Rmse: 0.3017
F-stat (1, 2): -inf, p-value: 1.0000
Degrees of Freedom: model 0, resid 2
-----------------------Summary of Estimated Coefficients------------------------
Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%
--------------------------------------------------------------------------------
x 0.0000 0.0000 0.00 0.9998 -0.0000 0.0000
intercept 0.0000 76683.4934 0.00 1.0000 -150299.6471 150299.6471
---------------------------------End of Summary---------------------------------
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