# Computing further characteristics

## Basic data adaptation

We can get several time series by joining data matrices Entry and Control.
You can get various pre-computed characteristics from these time series.

## Computing characteristics using TimeTransf

The goal of transformations made by TimeTransf module is to calculate values
(so called characteristics) for each time series in matrix Control. A characteristics
is for example an average weight of a patient. You can check a complete list of
preprocessed characteristics. The final preprocessed characteristics can be
downloaded and used.

It is possible to create your own characteristics by using TimeTransf module.
This page shows you how to do it. As an examaple
how the characteristics are defined in TimeTransf you can download the metabase
where they are already prepared.

You can also learn how to use the procedure TimeTransf to compute further
characteristics of these time series.

## Analysis of changes transformation steps

Analysis of changes explores how many times and when a patient have changed
his behaviour. For example the patient had smoked then in 1979 stopped and in
1980 started again. Please read further description.

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