The extrusion process stands as one of the most important technology of polymer processing in the industry. The inclusion of recycled plastic introduces material property variations, challenging the assumption of stability and fixed setpoints. Therefore, it becomes crucial to dynamically adapt parameters such as the motor speed and heating temperatures based on factors such as material output pressure, temperature and motor torque. Modelling the plastic extruder becomes valuable, offering insights into the its behavior, serving as a valuable asset to maintain consistent product quality despite variations in input material properties. Additionally, the model enables accurate evaluation of the extruder performance under different conditions and contexts. We have conducted an analysis of various virgin polypropylene plastic extruder behavior, treating the adaptation of its parameters as multivariate time series data. This allowed us to gain a comprehensive understanding of how the extruder parameters vary over time, specifically during the move from special grades of virgin polypropylene to another, wherein dealing with recycled plastics turns manageable and an intelligent forecasting model able to autonomously guiding both virgin and recycled plastic extrusion process can be developed.