Module 12. Assignment

>  Charge<-c(31.9,27,31.3,31,39.4,40.7,42.3,49.5,45,50,50.9,58.5,39.4,36.2,40.5,44.6,46.8,44.7,52.2,54,48.8 ,55.8,58.7,63.4)
>  Chargetimeseries <-ts(Charge)
>  Chargetimeseries <-ts(Charge,frequency=12,start=c(2012,1))
>  Chargetimeseries
      Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec
2012 31.9 27.0 31.3 31.0 39.4 40.7 42.3 49.5 45.0 50.0 50.9 58.5
2013 39.4 36.2 40.5 44.6 46.8 44.7 52.2 54.0 48.8 55.8 58.7 63.4
>  Chargeforecast<-HoltWinters(Chargetimeseries)
>  summary(Chargeforecast)
             Length Class  Mode     
fitted       48     mts    numeric  
x            24     ts     numeric  
alpha         1     -none- numeric  
beta          1     -none- numeric  
gamma         1     -none- numeric  
coefficients 14     -none- numeric  
seasonal      1     -none- character
SSE           1     -none- numeric  
call          2     -none- call 

> plot(Chargeforecast)



  
Alpha is how the model reacts to the data. Beta controls the trends, whether they  increase or decrease over time. Gamma controls the patterns of how often it spikes up or drops down. SSE is the difference between the actual data and the predicted  model.

In the graph, the black line represents the actual observed data points, and the   predicted values are in red. As shown in the graph, the red line is somewhat close   to, if not similar to, the black line's pattern, showing that the model is doing a  good job capturing the pattern in the data.
The close match between observed and fitted values suggests it provides a reliable forecast for predictions as long as the data continues to follow similar patterns.