The spectral properties of time series data reveal underlying processes but require complete datasets, often unavailable due to missing values and irregular sampling.This thesis uses a computational simulations framework to evaluate the perfor- mance of the Hybrid Wiener Interpolator [3], a novel method designed to reconstruct nonstationary time series data, thus making said data amenable for spectrum analysis. This research evaluates the Hybrid Wiener Interpolator's ability to handle nonstation- ary data and data gaps, comparing its performance to other interpolation methods under different stationarity and data integrity conditions. The results illuminate the robustness of this interpolator in scenarios typical of scientific datasets, offering a promising approach for enhancing spectrum estimation in the presence of non-ideal data conditions
Author Keywords: ARIMA Models, Data Imputation, Interpolation, Stationarity, Time Series, Time Series Simulations