A1924
Title: Integration of time series models with multicriteria methods for dynamic competitiveness assessment
Authors: Gabriella Epifani - University of Salento (Italy) [presenting]
Sabrina Maggio - University of Salento (Italy)
Pier Paolo Miglietta - University of Salento (Italy)
Abstract: The assessment of competitiveness in the olive oil market in southern Europe is often based on static representations, overlooking the temporal dynamics of performance. Traditional multicriteria decision analysis approaches rely on static or aggregated data, neglecting the stochastic structure of time series. A two-step methodological extension of the tensor-based TOPSIS approach is proposed by integrating time series models into the evaluation of alternatives. First, temporal dependence is analyzed by using time series tools, allowing persistence and variability patterns to be identified. Second, criteria and weights are determined through a data-driven approach based on the eigenvectors of the correlation matrix. This procedure enables the synthesis of the dependence structure among variables, avoiding subjective assignments. The proposed approach, which integrates multicriteria analysis and advanced statistical methods, is applied to southern European countries active in the olive oil market. Results show that incorporating temporal dynamics and data-driven weights significantly affects the ranking of alternatives compared to static approaches, providing a robust measure of long-term competitiveness.
