The SEMIFAR models are a new way to analyze trends in time series data. These models can handle different types of trends, like deterministic trends and long-term patterns. By using a mix of parametric and nonparametric methods, the SEMIFAR models can accurately capture the complexity of time series data. The researchers found that this approach can help data analysts better understand the underlying patterns in their data, whether it's short-term fluctuations or long-term trends. The method was tested on real-world data from various fields, showing its effectiveness in modeling different types of trends in time series data.