A Knowledge-Based System for Fashion Trend Forecasting

  • Paola Mello
  • Sergio Storari
  • Bernardo Valli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)


In this paper, we show how artificial intelligence techniques can be applied for the forecasting of trends in the high creative domain of fashion.

We describe a knowledge-based system that, starting from a set of keywords and pictures representing the concepts on which a fashion stylist chooses to base a new collection, is able to automatically create a trend forecast composed by the set of colors that better express these target concepts.

In order to model the knowledge used by the system to forecast trends, we experimented Bayesian networks. This kind of model is learned from a dataset of past trends by using different algorithms. We show how Bayesian networks can be used to make the forecast and the experiments made in order to evaluate their performances.


Bayesian Network Data Mining Technique Picture Color Conditional Probability Table Past Trend 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paola Mello
    • 1
  • Sergio Storari
    • 2
  • Bernardo Valli
    • 3
  1. 1.DEISUniversity of BolognaBolognaItaly
  2. 2.ENDIFUniversity of FerraraFerraraItaly
  3. 3.Faculty of SociologyUniversity of Urbino “Carlo Bo’”UrbinoItaly

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