Multicriteria decision support employing adaptive prediction in a
  tensor-based feature representation
Betania Silva Carneiro Campello Leonardo Tomazeli Duarte João Marcos Travassos Romano
Multicriteria decision analysis (MCDA) is a widely used tool to support
decisions in which a set of alternatives should be ranked or classified based
on multiple criteria. Recent studies in MCDA have shown the relevance of
considering not only current evaluations of each criterion but also past data.
Past-data-based approaches carry new challenges, especially in time-varying
environments. This study deals with this challenge via essential tools of
signal processing, such as tensorial representations and adaptive prediction.
More specifically, we structure the criteria' past data as a tensor and, by
applying adaptive prediction, we compose signals with these prediction values
of the criteria. Besides, we transform the prediction in the time domain into a
most favorable decision making domain, called the feature domain. We present a
novel extension of the MCDA method PROMETHEE II, aimed at addressing the tensor
in the feature domain to obtain a ranking of alternatives. Numerical
experiments were performed using real-world time series, and our approach is
compared with other existing strategies. The results highlight the relevance
and efficiency of our proposal, especially for nonstationary time series.