Probabilistic Linguistic Linear Least Absolute Regression for Fashion Trend Forecasting

  • Lisheng Jiang
  • Huchang LiaoEmail author
  • Zhi Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)


Fashion trend is an important aspect in costume designing given that the correct fashion trend prediction can help productions to occupy markets in short time. In the methods of forecast, fuzzy linear least absolute regression is a useful model. Meanwhile, most descriptions about the fashion trend are in nature words which are difficult to be used directly in present models. To deal with this problem, the probabilistic linguistic term set, a powerful tool in expressing and computing nature language, is introduced in this paper. First, operations on probabilistic linguistic term sets are modified to be more logical in the solution procedure of regression. Then a novel model which combines fuzzy linear least absolute regression and probabilistic linguistic term set is developed. Finally, an illustration about the forecast of clothing fashion trend is given to show the applicability of our method in costume designing evaluation.


Fashion trend forecasting Fuzzy linear least absolute regression Probabilistic linguistic term set Fuzzy-in and fuzzy-out 



The work was supported by the National Natural Science Foundation of China (71501135, 71771156), and the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Business SchoolSichuan UniversityChengduChina

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