scaling in time series I would like to know if it is fine to use  scaling(Time series * constant) before applying time series. Is it similar to data transformations( log , sqrt, Box-Cox) etc or is there any implication of using scaling? 
It would be great if some one can shed some light on above questions.
 A: In theory, scaling should make no difference whatsoever (beyond changing the residual variance and potential starting values). 
In practice, scaling a series may make numerical differences and even lead to different models being selected. For instance:
> library(forecast)
> 
> set.seed(1)
> foo <- arima.sim(model=list(ar=c(0.4,-0.2),ma=0.2),n=1e3)
> 
> auto.arima(foo)
Series: foo 
ARIMA(4,0,2) with zero mean 

Coefficients:
          ar1      ar2      ar3      ar4     ma1     ma2
      -0.9584  -0.0280  -0.1519  -0.1648  1.5267  0.5473
s.e.   0.9792   0.3898   0.1981   0.2560  0.9791  0.9474

sigma^2 estimated as 1.064:  log likelihood=-1447.29
AIC=2908.58   AICc=2908.69   BIC=2942.93
>
> auto.arima(1e9*foo)
Series: 1e+09 * foo 
ARIMA(0,0,0) with zero mean 

sigma^2 estimated as 1.439e+18:  log likelihood=-22324.11
AIC=44650.21   AICc=44650.22   BIC=44655.12

I have also seen pathological examples in which auto.arima() threw an error for a series, but scaling the series led to an estimable model. 
