Preference disaggregation analysis (PDA) is a widely used approach in multicriteria decision analysis that aims to extract preferential information from holistic judgments provided by decision makers. This paper presents an original methodological framework for PDA that addresses two significant challenges in this field. Firstly, it considers the multidimensional structure of data to capture decision makers' preferences based on descriptive measures of the criteria time series, such as trend and average. This novel approach enables an understanding of decision makers' preferences in decision-making scenarios involving time series analysis, which is common in medium- to long-term impact decisions. Secondly, the paper addresses the robustness issue commonly encountered in PDA methods by proposing a multi-objective and Monte Carlo simulation approach. This approach enables the consideration of multiple preference models and provides a mechanism to converge towards the most likely preference model. The proposed method is evaluated using real data, demonstrating its effectiveness in capturing preferences based on criteria and time series descriptive measures. The multi-objective analysis highlights the generation of multiple solutions, and, under specific conditions, reveals the possibility of achieving convergence towards a single solution that represents the decision maker's preferences.
