I am doing a regression on time series data. I have 60 lagged predictors which I will call x to predict a continuous variable y. I used the BoxCox function from the forecast package to transform y and then performed a linear regression of y on x. I did a one step ahead forecast over 5.5 months of data and found that the BoxCox function made the forecast error worse. I am using mae and rmse as critera for accuracy
It was better to simply perform no transformation. I am trying to reconcile this because I learned BoxCox in school and it was grilled into our heads that it is necessary to do but now it seems like it is a waste of time. Is my approach and conclusions about BoxCox right, or is simply that my data cannot benefit from BoxCox?