How can I recognize when I must apply "log transformation"? I have some time series - http://ww2.coastal.edu/kingw/statistics/R-tutorials/simplenonlinear.html
In this article author try to use log transformation for pressure data.
How can I recognize that data pressure must be transformed by log transformation ? - and I don't want to build plot 
 A: Please review When (and why) should you take the log of a distribution (of numbers)? .  I have programmed this in AUTOBOX ( a commercially available time series software package which I have helped develop)  which eliminates the normally required  visual/graphical analysis of the model errors by optionally/automatically performing the Box-Cox test. Notice I said model errors NOT the original data.  Note well that oftentimes the error variance changes deterministically in time. This is very important and has been virtually ignored . See http://www.unc.edu/~jbhill/tsay.pdf
More importantly you should not perform multiple regression on time series data due to opportunities/complications involved with time series analysis. Specifically identifying the appropriate/correct lag structure is impacted by auto-correlation in the data and Pulses/Level Shifts/Seasonal Pulses/Local Time Trends and changes in parameters over time and of course the homogeneity of the model error variance. See a blog I wrote discussing the differences between regression and Box-Jenkins http://www.autobox.com/cms/index.php/afs-university/intro-to-forecasting/doc_download/24-regression-vs-box-jenkins . Perhaps Prof. King might be interested in the pitfalls of using ordinary multiple regression (designed for cross-sectional data) when faced with time series data.
The good news is that ultimately a Transfer Function can be restated as a multiple regression with coefficients and lag structures.. This view is very useful in explaining the model in layman’s terms.
