Wavelet Diagnosis of Univariate Time Series Models – A Simulation Study

The research aims to study the effectiveness of wave diagnostics for single-variable time series models, by comparing a number of traditional methods based on Wave analysis, random data of various sizes (Small, Medium, Large) and multiple iterations were generated through data with specific specifications for the selection of hybrid models that combine simulation and Wave analysis, as well as the traditional exponential Bootstrap was compared with the hybrid model with waves through some statistical criteria.As well as comparing the performance of the traditional exponential bootstrap model with a hybrid model that combines seasonal exponential Bootstrap and the transformation of wave filters in the analysis and prediction of time series, through the application to the two models of a set of data with seasonal characteristics and high oscillations, the results showed that the hybrid model achieves higher accuracy in forecasting, and is characterized by its ability to isolate noise and sudden changes an effective option to improve the quality of forecasting, especially in contexts characterized by Stability or complex seasonality.