The article introduces a new method to figure out how many filters are needed to accurately model a time series. By using a time-varying autoregressive process and stable autoregressive filters, the researchers developed a way to select the right number of filters. They created a distance measure between filters and a reference curve to determine the best fit. The results show that this approach improves the performance of the model in predicting non-stationary time series.