Dynamic complexity
A measure that captures the richness of an unfolding — amplitude, frequency and distribution in one.
Dynamic complexity is a measure for time series that combines the amplitude and frequency of fluctuations with the distribution of measurement values across the range of the scale. It is usually calculated within a running window, so that the unfolding of the complexity itself becomes visible over time.
Where an average flattens a series into a single number, this measure tries precisely to capture how rich and how changeable an unfolding is. A time series that moves now calmly, now sharply, that shows both large and small excursions and uses the whole range of the scale, receives a high dynamic complexity. A flat, monotonous series a low one. It is, one might say, a measure of the liveliness of a signal.
Measuring an approaching transition
The clinical value lies above all in that running window. Because the measure renders the unfolding of complexity through time, it can make visible peaks that go together with a critical instability — the unrest that precedes an order transition. In Schiepek's Synergetic Navigation System dynamic complexity is therefore used to detect, on the basis of daily self-ratings, approaching turning points in a therapy course.
We note, as always, that a measure is an aid and not an oracle. The value of dynamic complexity lies not in a number by itself, but in what it helps make visible, together with the conversation and the clinical eye: that beneath a seemingly erratic unfolding a meaningful movement may be hidden, and that it is worth attending to it at the right moment.