Outliers can mess up ARIMA models, so a new method was created to find and fix them. The old ways had problems like mixing up different types of outliers and starting with bad guesses for the model. The new method uses two measures to find influential data points and groups of outliers. It also deals with the issue of missing some outliers in a sequence. The new method works well for spotting isolated or nearby outliers in time series data.