English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A number of Multiple Criteria Decision Analysis (MCDA) methods have beendeveloped to rank alternatives based on several decision criteria. Usually,MCDA methods deal with the criteria value at the time the decision is madewithout considering their evolution over time. However, it may be relevant toconsider the criteria' time series since providing essential information fordecision-making (e.g., an improvement of the criteria). To deal with thisissue, we propose a new approach to rank the alternatives based on the criteriatime-series features (tendency, variance, etc.). In this novel approach, thedata is structured in three dimensions, which require a more complex datastructure, as the \textit{tensors}, instead of the classical matrixrepresentation used in MCDA. Consequently, we propose an extension for theTOPSIS method to handle a tensor rather than a matrix. Computational resultsreveal that it is possible to rank the alternatives from a new perspective byconsidering meaningful decision-making information.

A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach