Single Exponential Smoothning

Since I somehow accidentally deleted the last file in https://github.com/statsmodels/statsmodels/pull/1274 but still have the code 
I decided to start from scratch and make the code in Pep8 style and focus on each individual Exponential smoothing (single double and triple) separately.
This will address https://github.com/statsmodels/statsmodels/issues/512 and  https://github.com/pydata/pandas/issues/2143


This code works good and can forecast for x amount of periods ahead using a bootstrapping method.

Improvements that are needed: .
*An solver for the alpha value using mean square error something like`sum(y-wdata)/len(y)`


*the Summary part needs RMS, Sum of squares residuals, AIC/AICc/BIC, Log-Likelihood, Swartz Criterion, average mean square error, Hanna-Quinn.

Here is what Eviews uses
http://www.eviews.com/EViews8/ev8ecets_n.html

Here is a set of test data to try
```
############Test######################
y = [47, 36, 12, 4, 84, 26, 37, 40, 72, 22]
print 'Exp=', ExpSmoothing(y, .5)

###forcasting
print 'forcast 10=', ExpSmoothing(y, .5, forecast = 10)
```

There is also working code in the answers to test against (without forcasting) here:
http://stackoverflow.com/questions/12726432/using-python-for-time-series-analysis-and-forecasting