  This paper shows how a time series of measurements of an evolving system can
be processed to create an inner time series that is unaffected by any
instantaneous invertible, possibly nonlinear transformation of the
measurements. An inner time series contains information that does not depend on
the nature of the sensors, which the observer chose to monitor the system.
Instead, it encodes information that is intrinsic to the evolution of the
observed system. Because of its sensor-independence, an inner time series may
produce fewer false negatives when it is used to detect events in the presence
of sensor drift. Furthermore, if the observed physical system is comprised of
non-interacting subsystems, its inner time series is separable; i.e., it
consists of a collection of time series, each one being the inner time series
of an isolated subsystem. Because of this property, an inner time series can be
used to detect a specific behavior of one of the independent subsystems without
using blind source separation to disentangle that subsystem from the others.
The method is illustrated by applying it to: 1) an analytic example; 2) the
audio waveform of one speaker; 3) video images from a moving camera; 4)
mixtures of audio waveforms of two speakers.
