  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.
