  A robust prediction model invoking the Takens embedding theorem, whose
\textit{working hypothesis} is obtained via an inference procedure based on the
minimum Fisher information principle, is presented. The coefficients of the
ansatz, central to the \textit{working hypothesis} satisfy a time independent
Schr\"{o}dinger-like equation in a vector setting. The inference of i) the
probability density function of the coefficients of the \textit{working
hypothesis} and ii) the establishing of constraint driven pseudo-inverse
condition for the modeling phase of the prediction scheme, is made, for the
case of normal distributions, with the aid of the quantum mechanical virial
theorem. The well-known reciprocity relations and the associated Legendre
transform structure for the Fisher information measure (FIM, hereafter)-based
model in a vector setting (with least square constraints) are self-consistently
derived. These relations are demonstrated to yield an intriguing form of the
FIM for the modeling phase, which defines the \textit{working hypothesis},
solely in terms of the observed data. Cases for prediction employing time
series' obtained from the: $(i)$ the Mackey-Glass delay-differential equation,
$(ii)$ one ECG sample from the MIT-Beth Israel Deaconess Hospital (MIT-BIH)
cardiac arrhythmia database, and $(iii)$ one ECG from the Creighton University
ventricular tachyarrhythmia database. The ECG samples were obtained from the
Physionet online repository. These examples demonstrate the efficiency of the
prediction model. Numerical examples for exemplary cases are provided.
