Hello,

I have been studying time series analysis (I still am) and I have a basic doubt: we start we our time data and need to apply transformations to it to make the data suitable for creating a model. The new transformed data becomes stationary, etc. we then build the model on the transformed data. The transformations seem to move us more and more away from the original data... Isn't the model supposed to describe the original data? How can the model do that if it is built on transformed data, which derives from the original data, but is very different from it? The original data may have had trend, seasonality, etc. and we remove those to build the model...But the goal is to build a model that describes and can make forecasts on the data that looks like the original data...

thank you!

^{Referred here by Forecasting: Principles and Practice, by Rob J Hyndman and George Athanasopoulos}