A method for training a machine-learning module of a computer-implemented prediction model for predicting product quality parameter values for one or more quality parameters of a chemical product produced by a chemical production plant. The production plant includes a plurality of sensors, each of which is configured to acquire process parameter values for one or more process parameters of a chemical process carried out by the production plant for producing the chemical product during operation of the production plant. A priori information about the production plant and the process carried out by the production plant is used, including chronological sequence information about a chronological sequence of the process carried out within the production plant, for which sensors sensor-specific time shifts between an acquisition time of training process parameter values and a production time of a product unit, during the production of which the corresponding training process parameter value was acquired.