  We introduce a method for quantifying the inherent unpredictability of a
continuous-valued time series via an extension of the differential Shannon
entropy rate. Our extension, the specific entropy rate, quantifies the amount
of predictive uncertainty associated with a specific state, rather than
averaged over all states. We relate the specific entropy rate to popular
`complexity' measures such as Approximate and Sample Entropies. We provide a
data-driven approach for estimating the specific entropy rate of an observed
time series. Finally, we consider three case studies of estimating specific
entropy rate from synthetic and physiological data relevant to the analysis of
heart rate variability.
