Error Bounds for Neutrosophic Period Estimation

Source abstract

Period estimation is a fundamental problem in signal processing, time-series analysis, oscillatory systems, and the study of approximately periodic phenomena. In many applica- tions, observations are not available as exact numerical values but are instead described by intervals reecting measurement uncertainty or incomplete information. This paper develops a deterministic framework for period estimation from interval-valued event times within the neutrosophic statistical perspective. We consider an approximately periodic sequence whose event times are represented by intervals. The midpoint of each interval is used to construct a least-squares estimator of the period. Explicit error bounds are established in terms of the interval radii and the number of observed events. We also derive weighted least-squares bounds, give conditions for identiability, and discuss the propagation of uncertainty to the estimated frequency. A numerical example illustrates the resulting bounds. The analysis distinguishes deterministic measurement uncertainty from random sampling variability. The proposed results provide veriable error guarantees under stated assumptions, rather than relying on unqualied claims of statistical superiority.