Made Exponential Smoothing for Statsmodel handles Single, double, triple & variations. (Needs Checking and improvements) 

Started Exponential Model off of code from dfrusdn and heavily modified. Handles 15 different models. The code is also fully documented. It looked like this was in demand so I tried out my coding skills.

This handles ALL of the following models:
 Simple Exponential Smoothing(SES)
 Simple Seasonal models (both multiplicative and additive)
 Brown's Linear Exponential Smoothing
 Holt's Double Exponential Smoothing
 Exponential trend method
 Damped-Trend Linear Exponential Smoothing
 Multiplicative damped trend (Taylor  2003)
 Holt-Winters Exponential Smoothing: multiplicative trend, additive trend, multiplicative season, additive season and damped models for all four variations

The code is accurate just that the initial values are off so they have to be adjusted. All the equations match what is given by Exponential smoothing: The state of the Art II page 8.

I do not know what methods are used to backcast the function so I left two Nans in the beginning to align the data. There is also a todo list.

I am not a programmer and I just did python for about 3 months so I need a code check. I think this should be in a class but I have not dealt with them enough to know what I am doing.

Testing: Go to the line 308 to 320 and remove the double pound signs for debug
```
#-------Test-------
#Parameters list
#(y, alpha, gamma, delta = 0, cycle = None, damp = 1, initial = None,
#                     trend = 'additive', forecast=None, season = 'additive', output='data')
y=[152,176,160,192,220,272,256,280,300,280,312,328]

exp_smoothing(y,0.2, 0.3)

>>> 
sdata= [      nan       nan  152.      172.8     195.072   218.3053  247.3936
  268.9406  290.2018  310.5986  322.2802  335.4886]
bdata= [     nan      nan  24.      23.04    22.8096  22.9367  24.7822  23.8116
  23.0465  22.2516  19.0806  17.3189]
cdata= [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
pdata= [      nan       nan  176.      195.84    217.8816  241.242   272.1758
  292.7523  313.2483  332.8503  341.3608  352.8076]

```
it matches the data in blue from http://analysights.wordpress.com/2010/05/20/forecast-friday-topic-double-exponential-smoothing/

For forecasting 3 periods ahead

```
exp_smoothing(y,0.2, 0.3,0,None,1, None, 'additive', 3)

Output:
pdata= [      nan       nan  176.      195.84    217.8816  241.242   272.1758
  292.7523  313.2483  332.8503  341.3608  352.8076  363.6765  360.4414
  379.522 ]
```