BUG: rolling_window yields unexpected results with win_type='triang'

[AllenDowney]

Here's the example in the documentation, modified to have non-zero mean:

n = 100
ser = pandas.Series(randn(n)+10, index=pandas.date_range('1/1/2000', periods=n))
pandas.rolling_window(ser, 5, 'triang').plot()
pandas.rolling_window(ser, 5, 'boxcar').plot()

The rolling boxcar window is centered around 10, as expected.

The triang window is centered around 6. That suggests that the weights in the window don't add up to 1.

Either that or my expectation of how it should work is wrong?

[jreback]

these are scipy dependent so

import scipy.signal as sig
sig.get_window(win_type, window)

yields the weights

[AllenDowney]

Understood. It looks like there is some logic in rolling_window that is
supposed to normalize the weights. Maybe that is not correct?

On Mon, Jun 30, 2014 at 11:13 AM, jreback [email protected] wrote:

these are scipy dependent so

import scipy.signal as sig
sig.get_window(win_type, window)

yields the weights

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#7618 (comment).

[jreback]

@AllenDowney certainly possible (not sure whether this should be normalized or not). Much of this I don't think is actually tested (as it relies on the weights from scipy). Want to put some test cases together?

[AllenDowney]

I don't have a pandas dev environment set up, but here's a simple case:

n = 10
ser = pandas.Series(np.ones(n))

mean = pandas.rolling_window(ser, 5, 'triang').mean()
np.testing.assert_approx_equal(mean, 1.0)

mean = pandas.rolling_window(ser, 5, 'gaussian', std=1.5).mean()
np.testing.assert_approx_equal(mean, 1.0)

mean = pandas.rolling_window(ser, 5, 'boxcar').mean()
np.testing.assert_approx_equal(mean, 1.0)

Currently only 'boxcar' is passing.

One theory: when rolling_window calls algos.roll_window, should it be
sending avg_wgt=True?

Allen

On Mon, Jun 30, 2014 at 11:48 AM, jreback [email protected] wrote:

@AllenDowney https://github.com/AllenDowney certainly possible (not
sure whether this should be normalized or not). Much of this I don't think
is actually tested (as it relies on the weights from scipy). Want to put
some test cases together?

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#7618 (comment).

[jreback]

In [10]: sig.get_window(('gaussian',1.5),5)
Out[10]: array([ 0.41111229,  0.8007374 ,  1.        ,  0.8007374 ,  0.41111229])

In [11]: sig.get_window('boxcar',5)
Out[11]: array([ 1.,  1.,  1.,  1.,  1.])

In [12]: sig.get_window('triang',5)
Out[12]: array([ 0.33333333,  0.66666667,  1.        ,  0.66666667,  0.33333333])

Do these need to be normalized?

[AllenDowney]

Yes, I think so.

On Mon, Jun 30, 2014 at 1:12 PM, jreback [email protected] wrote:

In [10]: sig.get_window(('gaussian',1.5),5)
Out[10]: array([ 0.41111229, 0.8007374 , 1. , 0.8007374 , 0.41111229])

In [11]: sig.get_window('boxcar',5)
Out[11]: array([ 1., 1., 1., 1., 1.])

In [12]: sig.get_window('triang',5)
Out[12]: array([ 0.33333333, 0.66666667, 1. , 0.66666667, 0.33333333])

Do these need to be normalized?

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#7618 (comment).

[jreback]

so why is 'boxcar' right then?

[AllenDowney]

Because the window is all 1s, it is already normalized.

On Mon, Jun 30, 2014 at 1:19 PM, jreback [email protected] wrote:

so why is 'boxcar' right then?

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#7618 (comment).

[marco-giancotti]

Indeed it appears to be a normalization issue. The result is correct (i.e. it overlaps with the original signal) if you multiply it by the following number:

window / sum(sig.get_window('triang', window))

[jreback]

@marco-giancotti @AllenDowney

either of you want to put some tests / fix in place for this?

ideally some comprehensive testing of all window types to validate this

[AllenDowney]

I might have time to do this next week, but would not be deeply offended if
someone else beat me to it.

Allen

On Thu, Aug 7, 2014 at 10:24 AM, jreback [email protected] wrote:

@marco-giancotti https://github.com/marco-giancotti @AllenDowney
https://github.com/AllenDowney

either of you want to put some tests / fix in place for this?

ideally some comprehensive testing of all window types to validate this

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#7618 (comment).

[stahlous]

I ran across this while looking into rolling_apply and got sidetracked thinking about rolling_window instead. I'm wondering if this function even makes sense in the default case of center=False for non-boxcar weights. If you have a time-series and apply a gaussian weighting to the rolling average where the most strongly weighted point is in the middle of the window, it seems strange that the computed mean would be correlated with the last index point in the window, at least by default.

This roughly illustrates my point:

s = pd.Series(np.sin(np.arange(0, 2*np.pi, 2*np.pi/20)))
s.plot()
pd.rolling_window(s, window=5, win_type='triang', std=1.0, center=False).plot()
pd.rolling_window(s, window=5, win_type='triang', std=1.0, center=True).plot()

I suppose this is nothing new, but it seems like a place where unaware users could get tripped up.