A review of symbolic analysis of experimental data

This article suggest a time-series analysis as a way to reduce noise in large databases when doing analysis. My first impression was, "you must be kidding time series have nothing to do with noise reduction". Then I did an experiment using my 4.7 terabytes of data and I found that a time series analysis could detect the cause of noise in my sample data (or training set). When I re-read the article after the experiment I found that this methodology is for processes that are non-linear and possible chaotic. I am using healthcare data that is non-linear and chaotic. I found that the time-series analysis was a good methodology to identify noise in the training set for the data tags=1. I still need to do a lot of reverse engineering to understand the why, but in the meantime I thought this was worthwhile passing on.