The article introduces models that can analyze time series data by detecting shifts in both average values and volatility levels. These models, called LS-ARFIMA and LS-GARCH, can capture long-term patterns, volatility, and sudden changes in data. The researchers developed test statistics to identify these shifts and used a method called quasi maximum likelihood estimation to estimate the models.