change in variable importance I have a multi year dataset. Each time frame  of the data has different predictor importance. 

Say for example, I am slicing the data into two partions as follows:


a dataset for the year 2014 (whole year)
a 2015 Jan. 


When i look for the predictor importance, the predictor variables are different for both the partions. (1) Hence i am not able to arrive at one  unique decision tree which can explain the model better. (2). I am not able to train a model which can predict the new data correctly.

Is there anything I am going wrong here.
Probably you can use multiple decision trees - each for slice of dataset. Then combine the results from each decision tree. You can also weigh the results from each decision tree. Eg the dataset for 2015 can be given more weight than dataset for 2014. I do not see much harm in spliting dataset and training different trees as it helps to account for the predictor values in a more accurate manner. If you could more details about the data or the "variable" you mentioned, people would be able to help more.