  A time series is uniquely represented by its geometric shape, which also
carries information. A time series can be modelled as the trajectory of a
particle moving in a force field with one degree of freedom. The force acting
on the particle shapes the trajectory of its motion, which is made up of
elementary shapes of infinitesimal neighborhoods of points in the trajectory.
It has been proved that an infinitesimal neighborhood of a point in a
continuous time series can have at least 29 different shapes or configurations.
So information can be encoded in it in at least 29 different ways. A 3-point
neighborhood (the smallest) in a discrete time series can have precisely 13
different shapes or configurations. In other words, a discrete time series can
be expressed as a string of 13 symbols. Across diverse real as well as
simulated data sets it has been observed that 6 of them occur more frequently
and the remaining 7 occur less frequently. Based on frequency distribution of
13 configurations or 13 different ways of information encoding a novel entropy
measure, called semantic entropy (E), has been defined. Following notion of
power in Newtonian mechanics of the moving particle whose trajectory is the
time series, a notion of information power (P) has been introduced for time
series. E/P turned out to be an important indicator of synchronous behaviour of
time series as observed in epileptic EEG signals.
