Holt Winter - how to use damped trends , boxcox tranfration and bias adjustments

anildas

question 1.

I am exploring Holt winter forecast model in one of our use case, would like to use the hyper parameters for

1) damped trends , 2)boxcox transformation and 3)bias adjustments

how to set these parameter in Rapidminer process?.

question 2.

Also I have used a python script to optimize the parameters for Holt Winters, and the result of optimized parameters are

Trend Damped    Seasonal      Seasonal Period            Box Cox     Remove bias Additive TRUE      Additive 12         FALSE TRUE

 How these parameters are equivalently mapped in Rapidminer Holt winter process with parameters alpha ,beta , gamma and length of period & seasonal model

Kindly advise.

anildas

Hi,: 

For easy readability , I have re-framed the second question as given below : 

Question 2.

I have used a python script to optimize the parameters(automate) for Holt Winters, and the result of optimized parameters gave me more accurate prediction (Python built model) than RM Holt-Winter model.

So please help me to map the equivalent hyper parameters to achieve the same prediction result in RM.

Python:

from statsmodels.tsa.holtwinters import ExponentialSmoothing

 model = ExponentialSmoothing(df, trend=Additive, damped=True, seasonal=Additive, seasonal_periods=12)

 model_fit = model.fit(optimized=True, use_boxcox=FALSE, remove_bias=TRUE)

RM

How these parameters are equivalently mapped in Rapidminer Holt winter process & forecasting 

(alpha, beta,gamma,length of period, seasonal model)