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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

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    $\begingroup$ Just curious, why not plot? $\endgroup$
    – zx8754
    Commented Aug 4, 2015 at 9:06
  • $\begingroup$ idea is that i will make multiple regression programmatically, and my program must use way without visual observe $\endgroup$
    – Max Usanin
    Commented Aug 4, 2015 at 9:09
  • $\begingroup$ What do you want to do with the result of your transformation? It looks dangerous to me to list for instance a mean after logarithmic transformation and a mean after no transformation in the same table. $\endgroup$
    – Dirk Horsten
    Commented Aug 4, 2015 at 9:37

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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.

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  • $\begingroup$ how I understand (or not), not necessary to make my time series more stationary for prepare to multiple regression - correct? $\endgroup$
    – Max Usanin
    Commented Aug 4, 2015 at 10:05
  • $\begingroup$ Essentially any required/needed transformation will be in the final equation. One doesn't necessarily have to pre-filter / pre-specify ALTHOUGH one can if they are careful. Again standard multiple regression procedures should be avoided like the plague as auto-correlation in the data can create havoc/opportunity. $\endgroup$
    – IrishStat
    Commented Aug 4, 2015 at 10:24
  • $\begingroup$ I am apologize maybe answers on my next question I will find in your links but still I want to ask in front: can you describe brief plan, what steps you will do if you want to do the regression analysis ?, I'm a little confused, and i do not know this tutorial can help me with multi regression or not (ww2.coastal.edu/kingw/statistics/R-tutorials/multregr.html) $\endgroup$
    – Max Usanin
    Commented Aug 4, 2015 at 11:00
  • $\begingroup$ When you have time series data you pre-filter the stationary x variable and apply the same filter to the stationary y series. Compute the cross-correlation between these two filtered series to aid the identification of the relationship between the original Y and X. One then identifies the necessary ARIMA structure and deterministic structure to augment the model. Test for necessity and sufficiency an culminate in a parsimonious model. See autobox.com/cms/index.php/afs-university/autobox-examples/… particularly section 2.3.3 $\endgroup$
    – IrishStat
    Commented Aug 4, 2015 at 11:39
  • $\begingroup$ this approach can be applied for multiple regression? $\endgroup$
    – Max Usanin
    Commented Aug 4, 2015 at 11:45

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