A sub-process sequence is identified from a temporal dataset. Based on time information, predictors are categorized as being available or not available during time periods. The predictors are used to make predictions of quantities that will occur in a future time period. The predictors are grouped into groups of a sequence of sub-processes, each including a grouping of one or more of the predictors. Information is output that allows a human being to modify the groups. The groups are finalized, responsive to any modifications. Prediction models are extracted based on dependencies between groups and sub-processes. A final predication model is determined based on a prediction model from the prediction models that best meets criteria. A dependency graph is generated based on the final prediction model. Information is output to display the final dependency graph for use by a user to adjust or not adjust elements of the sequential process.