# Box Cox Transformation makes Out of sample Forecast Error worse?

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?

• Your comment "I learned BoxCox in school and it was grilled into our heads" probably means that you were probably trained as an econometrician whom generally start with a model before they see the data rather than vice-versa or somewhere in between. Transformations are like drugs ... some are good for you and some are not. Please review stats.stackexchange.com/questions/18844/… for discussion that I think is relevant. – IrishStat Jan 16 '16 at 14:41
• BoxCox is a wonderful transformation because it encompasses wide range of transformation such as log, reciprocal, square root, cube root and more importantly $\textbf{no transformation}$. So if you user BoxCox transformation on the time series procedure correctly and if it doesn't require transformation, then box cox procedure should have recommended no transformation – forecaster Jan 16 '16 at 15:50
• Ahhh .. But the statistical requirements are on the errors from a model and not the original data.thus transforming the original series can easily lead to a false positive conclusion (other than a Box-Cox lambda of 1.0 ) as the original data has not been conditioned on a useful equation ( other than a simple mean model ) . What I am saying here is that you can easily get a false (Box-Cox coefficient reading by analyzing the original data. – IrishStat Jan 16 '16 at 17:11
• @forecaster ...thus it is not a placebo i.e. no downside effects as you are stating but a possibly (probably !) a rather dangerous (false) alternative to a useful model thus confounding/confusing the subsequent model identification phase – IrishStat Jan 16 '16 at 17:28
• .... see stats.stackexchange.com/questions/8955/… and review @probabilityislogic superb comments about when and why to transform . – IrishStat Jan 16 '16 at 18:04