I have daily time series data of almost two years starting for Jan 2018 to Nov 2019 and need to forecast for next two months Dec and Jan. My train data(Jan 2018-Aug 2019)is up to Aug 2019 and its error(MAPE) on test data(Sept to Nov) is 12%. I am using arimax technique.
Should i use these model to forecast for Dec-Jan which seems to too far ahead considering daily data or shall i build model on whole data again and then forecast for Dec -Jan?
My concern is if i am using first model to forecast then it is going too much into future and hence less confidence about the prediction. On the other hand if i use 2nd model then it's capturing recent changes and hopefully will give better forecast. My 2nd model is having all those regressors of 1st model whose p valus is less than 0.05.