A study of the Multicriteria decision analysis based on the time-series
  features and a TOPSIS method proposal for a tensorial approach
  A number of Multiple Criteria Decision Analysis (MCDA) methods have been
developed to rank alternatives based on several decision criteria. Usually,
MCDA methods deal with the criteria value at the time the decision is made
without considering their evolution over time. However, it may be relevant to
consider the criteria' time series since providing essential information for
decision-making (e.g., an improvement of the criteria). To deal with this
issue, we propose a new approach to rank the alternatives based on the criteria
time-series features (tendency, variance, etc.). In this novel approach, the
data is structured in three dimensions, which require a more complex data
structure, as the \textit{tensors}, instead of the classical matrix
representation used in MCDA. Consequently, we propose an extension for the
TOPSIS method to handle a tensor rather than a matrix. Computational results
reveal that it is possible to rank the alternatives from a new perspective by
considering meaningful decision-making information.
