Data Interpretation Driven by Algebra of Time-Dependent Operators

Huge volumes of data might to be containing hidden events that might to be associated to new laws or fundamental principles. If data follows polynomials forms then it can be related to abstract operations belonging to a concrete commutator of a pair of operators exhibiting a well-defined algebra. In this paper, a kind of algebra defined by a set of operators and eigenvalue equations, is proposed. It is hypothesized that resultant eigenvalues might to be correlated to data behavior. In order to illustrate the proposed formalism, the cases of pandemics and global inflation, whose data behavior might to some extent be dictated by algebra rules, are presented. Despite algebra seems to be composed by nonlinear elements these would explain apparition of morphologies in data, such as peaks in time, displacement as well as periodicity of them along the time where dara was acquired. Comparisons between model and real data are presented and discussed.