The article discusses how to interpret partial autocorrelation functions of seasonal time series models. The researchers from Queen's University in Ontario aimed to clarify the relationship between these functions and seasonal patterns in data. They found that by analyzing these functions, one can better understand the underlying seasonal trends in time series data.