Momentary information transfer as a coupling measure of time series

We propose a method to analyze couplings between two simultaneously measured time series. Our approach is based on conditional mutual sorting information. It is related to other concepts for detecting coupling directions: the old idea of Marko for directed information and the more recent concept of Schreiber's transfer entropy. By setting suitable conditions we first of all consider momentary information in both time series. This enables the detection not only of coupling directions but also delays. Sorting information refers to ordinal properties of time series, which makes the analysis robust with respect to strictly monotonous distortions and thus very useful in the analysis of proxy data in climatology. Fortunately, ordinal analysis is easy and fast to compute. We consider also the problem of reliable estimation from finite time series.